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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Individual, Model-independent Masses of the Closest Known Brown Dwarf Binary to the Sun

At a distance of ∼2 pc, our nearest brown dwarf neighbor, Luhman 16 AB, has been extensively studied since its discovery 3 years ago, yet its most fundamental parameter—the masses of the individual dwarfs—has not been constrained with precision. In this work, we present the full astrometric orbit and barycentric motion of Luhman 16 AB and the first precision measurements of the individual component masses. We draw upon archival observations spanning 31 years from the European Southern Observatory (ESO) Schmidt Telescope, the Deep Near-Infrared Survey of the Southern Sky (DENIS), public FORS2 data on the Very Large Telescope (VLT), and new astrometry from the Gemini South Multiconjugate Adaptive Optics System (GeMS). Finally, we include three radial velocity measurements of the two components from VLT/CRIRES, spanning one year. With this new data sampling a full period of the orbit, we use a Markov chain Monte Carlo algorithm to fit a 16-parameter model incorporating mutual orbit and barycentric motion parameters and constrain the individual masses to be 27.9{sub −1.0}{sup +1.1} M{sub J} for the T dwarf and 34.2{sub −1.1}{sup +1.3} M{sub J} for the L dwarf. Our measurements of Luhman 16 AB’s mass ratio and barycentric motion parameters are consistent with previous estimates in the literature utilizing recent astrometry only. The GeMS-derived measurements of the Luhman 16 AB separation in 2014–2015 agree closely with Hubble Space Telescope (HST) measurements made during the same epoch, and the derived mutual orbit agrees with those measurements to within the HST uncertainties of 0.3–0.4 mas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Biogeochemical cycling at the aquatic–terrestrial interface is linked to parafluvial hyporheic zone inundation history

Abstract. The parafluvial hyporheic zone combines the heightened biogeochemical and microbial interactions indicative of a hyporheic region with direct atmospheric/terrestrial inputs and the effects of wet–dry cycles. Therefore, understanding biogeochemical cycling and microbial interactions in this ecotone is fundamental to understanding biogeochemical cycling at the aquatic–terrestrial interface and to creating robust hydrobiogeochemical models of dynamic river corridors. We aimed to (i) characterize biogeochemical and microbial differences in the parafluvial hyporheic zone across a small spatial domain (6 lateral meters) that spans a breadth of inundation histories and (ii) examine how parafluvial hyporheic sediments respond to laboratory-simulated re-inundation. Surface sediment was collected at four elevations along transects perpendicular to flow of the Columbia River, eastern WA, USA. The sediments were inundated by the river 0, 13, 127, and 398 days prior to sampling. Spatial variation in environmental variables (organic matter, moisture, nitrate, glucose, % C, % N) and microbial communities (16S and internal transcribed spacer (ITS) rRNA gene sequencing, qPCR) were driven by differences in inundation history. Microbial respiration did not differ significantly across inundation histories prior to forced inundation in laboratory incubations. Forced inundation suppressed microbial respiration across all histories, but the degree of suppression was dramatically different between the sediments saturated and unsaturated at the time of sample collection, indicating a binary threshold response to re-inundation. We present a conceptual model in which irregular hydrologic fluctuations facilitate microbial communities adapted to local conditions and a relatively high flux of CO2. Upon rewetting, microbial communities are initially suppressed metabolically, which results in lower CO2 flux rates primarily due to suppression of fungal respiration. Following prolonged inundation, the microbial community adapts to saturation by shifting composition, and the CO2 flux rebounds to prior levels due to the subsequent change in respiration. Our results indicate that the time between inundation events can push the system into alternate states: we suggest (i) that, above some threshold of inundation interval, re-inundation suppresses respiration to a consistent, low rate and (ii) that, below some inundation interval, re-inundation has a minor effect on respiration. Extending reactive transport models to capture processes that govern such dynamics will provide more robust predictions of river corridor biogeochemical function under altered surface water flow regimes in both managed and natural watersheds.

58 GEOSCIENCES↗
NASA NTRS1983

The effects of hypergravity on the rate of antibody formation in the rat

This experiment was designed to measure the immune response in acutely stressed and chronically centrifuged hyper-G-adapted male rats. Rats were exposed to 2.1 and 3.1 G. Acutely stressed animals were injected with sheep red blood cells (SRBC) on the day of inital exposure to hyper-G, and were chronically centrifuged for 10 to 15 days after immunization. Hyper-G-adapted rats were chronically centrifuged for 28 days prior to antigen injection and for 21 days after injection. Booster injections were given and serum samples taken at intervals from 3 to 9 days after the initial and booster injections. Antigen dose, injected ip, ranged between 1.35 x 10 to the 6th and 1.38 x 10 to the 9th SRBC/100 g. body weight. Pair-fed and ad libitum fed noncentrifuged controls were used. No significant differences in anti-SRBC antibody titers were found between centrifuged and control animals, although there were some changes in WBC counts and a significant increase in adrenal-gland size in acutely stressed animals.

Scibetta, S. M.↗
NASA NTRS1988

Diode laser harmonic spectroscopy applied to in situ measurements of atmospheric trace molecules

With the emergence of lead-salt diode laser technology, a broad and important IR spectral region, roughly 3-30 microns, became accessible to tunable laser spectroscopy. More recent advances in fabrication techniques have provided experimenters with relatively high power quasi-single-mode high-temperature lasers that are readily adapted to compact automated instruments for field experiments. An especially attractive capability of diode lasers is the ease of using them for simultaneous multiconstituent sampling in the atmosphere. This paper presents a summary description of field instruments for atmospheric research which employ diode lasers and second-harmonic detection. Representative results obtained with some of these instruments are presented.

Loewenstein, Max↗
NASA NTRS1991

On the optimality of code options for a universal noiseless coder

A universal noiseless coding structure was developed that provides efficient performance over an extremely broad range of source entropy. This is accomplished by adaptively selecting the best of several easily implemented variable length coding algorithms. Custom VLSI coder and decoder modules capable of processing over 20 million samples per second are currently under development. The first of the code options used in this module development is shown to be equivalent to a class of Huffman code under the Humblet condition, other options are shown to be equivalent to the Huffman codes of a modified Laplacian symbol set, at specified symbol entropy values. Simulation results are obtained on actual aerial imagery, and they confirm the optimality of the scheme. On sources having Gaussian or Poisson distributions, coder performance is also projected through analysis and simulation.

Yeh, Pen-Shu↗
NASA NTRS1990

Space Station Payload Adaptation System

The development and design of a system of containers for the efficient integration of Space Station payloads is described called the Space Station Payload Adaptation System (SSPAS). The SSPAS was developed to address the incorporation of multiple payloads, the use of a small payload carrier, large numbers of samples, and on-orbit servicing. SSPAS subsystems such as the Spacelab rack are modular and designed for integration into the 'Quick Is Beautiful' (QIB) facility. The QIB is designed to provide access to space for small- and medium-sized microgravity research projects and proof-of-concept investigations. The power-distribution and heat-rejection potential of the QIB are described, and an improved experiment-apparatus container is proposed. The SSPAS rack-mounting and container concepts are concluded to make up an efficent system that can effectively exploit the R&D potential of the Space Station.

Taylor, Kenneth R.↗
NASA NTRS1986

Radiation Sensor

Claypack is a cost-effective portable system developed by Barringer Research Ltd. for rapid on-site analysis of clay minerals. It is an adaptation of a hand-held rationing radiometer. By measuring the intensity of reflected radiation, the device discriminates among different minerals present in a sample. It simultaneously analyzes radiation intensities in two separate bands of the spectrum, and calculates the ratio of one to the other. The "reflectance ratio" is computer processed and displayed in digital form.

Source record↗
NASA NTRS2000

Adaptive Detector Arrays for Optical Communications Receivers

The structure of an optimal adaptive array receiver for ground-based optical communications is described and its performance investigated. Kolmogorov phase screen simulations are used to model the sample functions of the focal-plane signal distribution due to turbulence and to generate realistic spatial distributions of the received optical field. This novel array detector concept reduces interference from background radiation by effectively assigning higher confidence levels at each instant of time to those detector elements that contain significant signal energy and suppressing those that do not. A simpler suboptimum structure that replaces the continuous weighting function of the optimal receiver by a hard decision on the selection of the signal detector elements also is described and evaluated. Approximations and bounds to the error probability are derived and compared with the exact calculations and receiver simulation results. It is shown that, for photon-counting receivers observing Poisson-distributed signals, performance improvements of approximately 5 dB can be obtained over conventional single-detector photon-counting receivers, when operating in high background environments.

Vilnrotter, V.↗
NASA NTRSDate not supplied

Kamodo’s Satellite Constellation Mission Planning Tool

Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.

software↗
NASA NTRSDate not supplied

SM25C-2002: Kamodo’s Satellite Constellation Mission Planning Tool

Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.

python↗
DOE OSTI2019

MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing speed. Binning a typical metagenome assembly takes only a few minutes on a single commodity workstation. We therefore recommend the community adopts MetaBAT 2 for their metagenome binning experiments. MetaBAT 2 is open source software and available at https://bitbucket.org/berkeleylab/metabat .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Confocal Raman Microscopy as an In Situ Probe of Volume Change in Hydration-Sensitive Polymer Membranes

An approach is described for measuring hydration-induced volume change within ion conductive polymer membranes and thin films by adapting the technique of confocal Raman microscopy. With careful consideration of factors that affect excitation and scattering within the confocal probe volume region, material swelling and deswelling were estimated from the intensities of polymer matrix spectral features. A high numerical aperture oil-immersion objective was used to achieve tight focusing within samples and the efficient collection of Raman scattered light. The approach is demonstrated for a fluorinated cation-exchange ionomer (Nafion) and the hydrocarbon anion-exchange ionomer Sustainion. Samples were monitored while under a nitrogen atmosphere that was cycled between dry and humid (∼50% relative humidity, RH) states. The volume changes estimated from the Raman spectra were in close agreement with those derived from conventional measurements. The reported work advances understanding needed to adapt confocal Raman microscopy for quantitative in situ and operando studies of ionomers within electrochemical devices, such as polymer electrolyte membrane fuel cells and electrolyzers, and lays a foundation for broader applications in the study of hydration-induced polymer swelling and the associated molecular level water and polymer framework structural changes.

Absorption↗
NASA NTRS2017

Novel End-to-End Molecular Biology Approach for Direct Nanopore 1D cDNA Sequencing of Reverse Transcribed mRNAs Purified from Cell Cultures by the NASA ISS WetLab2 SPM

Continued space bioscience research onboard the International Space Station (ISS) and future long-duration flight missions to the Moon or Mars will require the ability to conduct on-orbit molecular analysis of biological samples independently from Earth. In the last year two new molecular analytic technologies have been installed and the technologies demonstrated onboard the ISS: The Sample Prep Module (SPM) WetLab-2 (WL2) qRT-PCR toolbox and the Oxford Nanopore MinIon Biomolecule Sequencer. Here we describe protocol development and integration into existing ISS technology for end-to-end on-orbit biological sample processing and molecular analysis with real time results generated utilizing only field offline analytic software. For this experiment we isolated primary cells from bone marrow flushes of wild type B6129SF2 mice (Jackson Labs) long bones. The cell isolate was then processed using the SPM to produce total 147nanograms of RNA. The total RNA was purified to only messenger RNA (mRNA) and transferred to Smartcycler Thermocycle ISS kit consumable tube using Eppendorf gel loading pipette tips for further processing. Complementary first strand cDNA was synthesized using OLIGO dT priming followed by addition of SuperScript II Reverse Transcriptase and thermal cycling as per manufacturers instruction. All thermal cycling was conducted using the ISS WetLab-2 Cephid Smarcycler real time thermal cycler. Our protocol takes advantage of mRNAs native poly(A) tail, synthesized in vivo to protect the mRNA from degradation by endonucleases, to eliminate end-prep for adapter ligation. The adapted library is purified using MyOne C1 Streptavidin beads before elution in buffer. The pre-sequencing library is diluted in the loading buffer and injected into the MinIon sample port, drawn into the nanopore window by capillary action, and sequenced using the MinKnown software with local basecalling. The sequencing read produced 34.5 million events and local basecalling produced 117,301 successful reads. NCBI Blast of the data for the mouse genome resulted in 2,462 successful nucleotide collection matches (gene sequences) exceeding 70 homology. These results demonstrate the viability of this novel flight ready end-to-end sample analytic methodology and provide a real time homolog for flight experimentation utilizing supply kits and technologies that have already been demonstrated on ISS.

MinIon↗
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2022

Field validation of data-driven BSDF and peak extraction models for light-scattering fabric shades

Shading and daylighting systems affect cooling, heating, and lighting energy use by modulating solar radiation through the building façade. Characterizing shading systems holistically and accurately helps designers and engineers evaluate shading systems to achieve energy and non-energy performance goals. These complex fenestration systems can be modeled using Bidirectional Scattering Distribution Functions (BSDF), which map incident radiation to hemispherical distributions of outgoing radiation. Data-driven, tabulated BSDFs are derived from interpolated goniophotometer measured data, then sampled during the raytracing calculation. A peak extraction (PE) algorithm was developed to circumvent limits in BSDF angular resolution, where the specular peak is extracted during simulation by evaluating the BSDF in the through direction and surrounding region. The objective of this study was to validate this measurement and modeling workflow using field monitored data from a full scale testbed with eleven installed fabrics of different weaves, openness factors, and colors and assess the accuracy of the workflow under different adaptation and contrast conditions. Test conditions were limited to clear sky conditions with the sun in the field of view. Results showed that, for tensor tree datasets, vertical illuminance, solar luminance (2.5° apex), and daylight glare probability (DGP) were predicted to within a mean bias error (MBE) error of -456 lx (-12.3%), -3.46e5 (-38.4%), and -0.042 (-7.8%) when full PE occurred. With a binary classification of glare/ no glare, DGP was predicted accurately with a true positive rate of 0.98 and true negative rate of 1.0 using tensor tree data and less accurately with Klems BSDF data, particularly for cases of no glare. The workflow may be of insufficient accuracy to distinguish borderline performance between fabrics using the four-point glare scale, particularly under low adaptation, high contrast daylit conditions. Errors were due to reductions in peak shape and intensity across the BSDF interpolation and data reduction workflow. Future work is needed to better preserve measurement fidelity during interpolation and sampling, which in turn will improve PE performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

The SINS/zC-SINF survey of z ∼ 2 galaxy kinematics: Evidence for powerful active galactic nucleus-driven nuclear outflows in massive star-forming galaxies

We report the detection of ubiquitous powerful nuclear outflows in massive (≥10{sup 11} M {sub ☉}) z ∼ 2 star-forming galaxies (SFGs), which are plausibly driven by an active galactic nucleus (AGN). The sample consists of the eight most massive SFGs from our SINS/zC-SINF survey of galaxy kinematics with the imaging spectrometer SINFONI, six of which have sensitive high-resolution adaptive optics-assisted observations. All of the objects are disks hosting a significant stellar bulge. The spectra in their central regions exhibit a broad component in Hα and forbidden [N II] and [S II] line emission, with typical velocity FWHM ∼ 1500 km s{sup –1}, [N II]/Hα ratio ≈ 0.6, and intrinsic extent of 2-3 kpc. These properties are consistent with warm ionized gas outflows associated with Type 2 AGN, the presence of which is confirmed via independent diagnostics in half the galaxies. The data imply a median ionized gas mass outflow rate of ∼60 M {sub ☉} yr{sup –1} and mass loading of ∼3. At larger radii, a weaker broad component is detected but with lower FWHM ∼485 km s{sup –1} and [N II]/Hα ≈ 0.35, characteristic for star formation-driven outflows as found in the lower-mass SINS/zC-SINF galaxies. The high inferred mass outflow rates and frequent occurrence suggest that the nuclear outflows efficiently expel gas out of the centers of the galaxies with high duty cycles and may thus contribute to the process of star formation quenching in massive galaxies. Larger samples at high masses will be crucial in confirming the importance and energetics of the nuclear outflow phenomenon and its connection to AGN activity and bulge growth.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Grid and basis adaptive polynomial chaos techniques for sensitivity and uncertainty analysis

The demand for accurate and computationally affordable sensitivity and uncertainty techniques is constantly on the rise and has become especially pressing in the nuclear field with the shift to Best Estimate Plus Uncertainty methodologies in the licensing of nuclear installations. Besides traditional, already well developed methods – such as first order perturbation theory or Monte Carlo sampling – Polynomial Chaos Expansion (PCE) has been given a growing emphasis in recent years due to its simple application and good performance. This paper presents new developments of the research done at TU Delft on such Polynomial Chaos (PC) techniques. Our work is focused on the Non-Intrusive Spectral Projection (NISP) approach and adaptive methods for building the PCE of responses of interest. Recent efforts resulted in a new adaptive sparse grid algorithm designed for estimating the PC coefficients. The algorithm is based on Gerstner's procedure for calculating multi-dimensional integrals but proves to be computationally significantly cheaper, while at the same it retains a similar accuracy as the original method. More importantly the issue of basis adaptivity has been investigated and two techniques have been implemented for constructing the sparse PCE of quantities of interest. Not using the traditional full PC basis set leads to further reduction in computational time since the high order grids necessary for accurately estimating the near zero expansion coefficients of polynomial basis vectors not needed in the PCE can be excluded from the calculation. Moreover the sparse PC representation of the response is easier to handle when used for sensitivity analysis or uncertainty propagation due to the smaller number of basis vectors. The developed grid and basis adaptive methods have been implemented in Matlab as the Fully Adaptive Non-Intrusive Spectral Projection (FANISP) algorithm and were tested on four analytical problems. These show consistent good performance both in terms of the accuracy of the resulting PC representation of quantities and the computational costs associated with constructing the sparse PCE. Basis adaptivity also seems to make the employment of PC techniques possible for problems with a higher number of input parameters (15–20), alleviating a well known limitation of the traditional approach. The prospect of larger scale applicability and the simplicity of implementation makes such adaptive PC algorithms particularly appealing for the sensitivity and uncertainty analysis of complex systems and legacy codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Adaptive Generation of Training Data for ML Reduced Model Creation

Machine learning proxy models are often used to speed up or completely replace complex computational models. The greatly reduced and deterministic computational costs enable new use cases such as digital twin control systems and global optimization. The challenge of building these proxy models is generating the training data. A naive uniform sampling of the input space can result in a non-uniform sampling of the output space of a model. This can cause gaps in the training data coverage that can miss finer scale details resulting in poor accuracy. While larger and larger data sets could eventually fill in these gaps, the computational burden of full-scale simulation codes can make this prohibitive. In this paper, we present an adaptive data generation method that utilizes uncertainty estimation to identify regions where training data should be augmented. By targeting data generation to areas of need, representative data sets can be generated efficiently. The effectiveness of this method will be demonstrated on a simple one-dimensional function and a complex multidimensional physics model.

Cianciosa, Mark↗