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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Lossless Astronomical Image Compression and the Effects of Random Noise

In this paper we compare a variety of modern image compression methods on a large sample of astronomical images. We begin by demonstrating from first principles how the amount of noise in the image pixel values sets a theoretical upper limit on the lossless compression ratio of the image. We derive simple procedures for measuring the amount of noise in an image and for quantitatively predicting how much compression will be possible. We then compare the traditional technique of using the GZIP utility to externally compress the image, with a newer technique of dividing the image into tiles, and then compressing and storing each tile in a FITS binary table structure. This tiled-image compression technique offers a choice of other compression algorithms besides GZIP, some of which are much better suited to compressing astronomical images. Our tests on a large sample of images show that the Rice algorithm provides the best combination of speed and compression efficiency. In particular, Rice typically produces 1.5 times greater compression and provides much faster compression speed than GZIP. Floating point images generally contain too much noise to be effectively compressed with any lossless algorithm. We have developed a compression technique which discards some of the useless noise bits by quantizing the pixel values as scaled integers. The integer images can then be compressed by a factor of 4 or more. Our image compression and uncompression utilities (called fpack and funpack) that were used in this study are publicly available from the HEASARC web site.Users may run these stand-alone programs to compress and uncompress their own images.

Pence, William↗
DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗
DOE OSTI2025

Rippled metamaterials with scale-dependent tailorable elasticity

Thermally induced ripples are intrinsic features of nanometer-thick films, atomically thin materials, and cell membranes, significantly affecting their elastic properties. Despite decades of theoretical studies on the mechanics of suspended thermalized sheets, controversy still exists over the impact of these ripples, with conflicting predictions about whether elasticity is scale-dependent or scale-independent. Experimental progress has been hindered so far by the inability to have a platform capable of fully isolating and characterizing the effects of ripples. This knowledge gap limits the fundamental understanding of thin materials and their practical applications. Here, we show that thermal-like static ripples shape thin films into a class of metamaterials with scale-dependent, customizable elasticity. Utilizing a scalable semiconductor manufacturing process, we engineered nanometer-thick films with precisely controlled frozen random ripples, resembling snapshots of thermally fluctuating membranes. Resonant frequency measurements of rippled cantilevers reveal that random ripples effectively renormalize and enhance the average bending rigidity and sample-to-sample variations in a scale-dependent manner, consistent with recent theoretical estimations. The predictive power of the theoretical model, combined with the scalability of the fabrication process, was further exploited to create kirigami architectures with tailored bending rigidity and mechanical metamaterials with delayed buckling instability.

Applied Physical Sciences↗
DOE OSTI2023

Graph Sparsification by Approximate matrix Multiplication

Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storage bottlenecks. One way of sparsifying a weighted graph, while sharing the same vertices as the original graph but reducing the number of edges, is through spectral sparsification. We study this problem through the perspective of RandNLA. Specifically, we utilize randomized matrix multiplication to give a clean and simple analysis of how sampling according to edge weights gives a spectral approximation to graph Laplacians, without requiring spectral information. Through the CR–MM algorithm, we attain a simple and computationally efficient sparsifier whose resulting Laplacian estimate is unbiased and of minimum variance. Here, we define a new notion of additive spectral sparsifiers, which has not been considered in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Sci-Thur AM: YIS – 06: A Monte Carlo study of macro- and microscopic dose descriptors and the microdosimetric spread using detailed cellular models

Purpose: To develop Monte Carlo models of cell clusters to investigate the relationships between macro- and microscopic dose descriptors, quantify the microdosimetric spread in energy deposition for subcellular targets, and determine how these results depend on the computational model. Methods: Microscopic tissue structure is modelled as clusters of 13 to 150 cells, with cell (nuclear) radii between 5 and 10 microns (2 and 9 microns). Energy imparted per unit mass (specific energy or dose) is scored in the nucleus (D{sub nuc}) and cytoplasm (D{sub cyt}) for incident photon energies from 20 to 370 keV. Dose-to-water (D{sub w,m}) and dose-to-medium (D{sub m,m}) are compared to D{sub nuc} and D{sub cyt}. Single cells and single nuclear cavities are also simulated. Results: D{sub nuc} and D{sub cyt} are sensitive to the surrounding environment with deviations of up to 13% for a single nucleus/cell compared with a multicellular cluster. These dose descriptors vary with cell and nucleus size by up to 10%. D{sub nuc} and D{sub cyt} differ from D{sub w,m} and D{sub m,m} by up to 32%. The microdosimetric spread is sensitive to whether cells are arranged randomly or in a hexagonal lattice, and whether subcellular compartment sizes are sampled from a normal distribution or are constant throughout the cluster. Conclusions: D{sub nuc} and D{sub cyt} are sensitive to cell morphology, elemental composition and the presence of surrounding cells. The microdosimetric spread was investigated using realistic elemental compositions for the nucleus and cytoplasm, and depends strongly on subcellular compartment size, source energy and dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

HIV-1 Envelope Glycoproteins from Diverse Clades Differentiate Antibody Responses and Durability among Vaccinees

Induction of broadly cross-reactive antiviral humoral responses with the capacity to target globally diverse circulating strains is a key goal for HIV-1 immunogen design. A major gap in the field is the identification of diverse HIV-1 envelope antigens to evaluate vaccine regimens for binding antibody breadth. In this study, we define unique antigen panels to map HIV-1 vaccine-elicited antibody breadth and durability. Diverse HIV-1 envelope glycoproteins were selected based on genetic and geographic diversity to cover the global epidemic, with a focus on sexually acquired transmitted/founder viruses with a tier 2 neutralization phenotype. Unique antigenicity was determined by nonredundancy (Spearman correlation), and antigens were clustered using partitioning around medoids (PAM) to identify antigen diversity. Cross-validation demonstrated that the PAM method was better than selection by reactivity and random selection. Analysis of vaccine-elicited V1V2 binding antibody in longitudinal samples from the RV144 clinical trial revealed the striking heterogeneity among individual vaccinees in maintaining durable responses. These data support the idea that a major goal for vaccine development is to improve antibody levels, breadth, and durability at the population level. Elucidating the level and durability of vaccine-elicited binding antibody breadth needed for protection is critical for the development of a globally efficacious HIV vaccine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Active Learning Framework

Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.

Nebgen, Benjamin↗
DOE OSTI2015

The influence of age and gender on skin-associated microbial communities in urban and rural human populations

Differences in the bacterial community structure associated with 7 skin sites in 71 healthy people over five days showed significant correlations with age, gender, physical skin parameters, and whether participants lived in urban or rural locations in the same city. While body site explained the majority of the variance in bacterial community structure, the composition of the skin-associated bacterial communities were predominantly influenced by whether the participants were living in an urban or rural environment, with a significantly greater relative abundance of Trabulsiella in urban populations. Adults maintained greater overall microbial diversity than adolescents or the elderly, while the intragroup variation among the elderly and rural populations was significantly greater. Lastly, skin-associated bacterial community structure and composition could predict whether a sample came from an urban or a rural resident ~ 5x greater than random.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data Science and Machine Learning for Genome Security

This report describes research conducted to use data science and machine learning methods to distinguish targeted genome editing versus natural mutation and sequencer machine noise. Genome editing capabilities have been around for more than 20 years, and the efficiencies of these techniques has improved dramatically in the last 5+ years, notably with the rise of CRISPR-Cas technology. Whether or not a specific genome has been the target of an edit is concern for U.S. national security. The research detailed in this report provides first steps to address this concern. A large amount of data is necessary in our research, thus we invested considerable time collecting and processing it. We use an ensemble of decision tree and deep neural network machine learning methods as well as anomaly detection to detect genome edits given either whole exome or genome DNA reads. The edit detection results we obtained with our algorithms tested against samples held out during training of our methods are significantly better than random guessing, achieving high F1 and recall scores as well as with precision overall.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

The effect of anisotropic emission from thick accretion disks on the luminosity functions of active galactic nuclei

High-luminosity active galactic nuclei (AGNs) powered by accretion onto a massive black hole (or other compact object) may have bolometric luminosities dominated by thermal emission from a geometrically thick accretion disk. Radiation from these disks is strongly anisotropic, which has important consequences for the observed luminosity distribution, and therefore for systematic biases in flux-limited samples. The effect of anisotropic emission from an ensemble of AGNs with random oriented thick disks radiating at or near the Eddington limit is calculated. Because of their higher luminosities, it is predicted face-on disks should constitute an increasing fraction of observed high-redshift, high-luminosity AGNs. Comparison of the results with observed quasar luminosity functions suggests a narrow mass distribution with an upper limit of about a billion solar masses for high-redshift quasars.

Urry, C. M.↗
NASA NTRS1994

Atomic oxygen durability evaluation of the flexible batten for the photovoltaic array mast on Space Station

A test program was conducted at the National Aeronautics and Space Administration's Lewis Research Center (LeRC) to evaluate the long term low Earth orbital (LEO) atomic oxygen (AO) durability of a flexible (fiberglass-epoxy composite) batten. The flexible batten is a component used to provide structural rigidity in the photovoltaic array mast on Space Station. The mast is used to support and articulate the photovoltaic array, therefore, the flexible batten must be preloaded for the 15 year lifetime of an array blanket. Development hardware and composite materials were evaluated in ground testing facilities for AO durability and dynamic retraction-deployment cyclic loading representative of expected full life in-space application. The CV1144 silicone (AO protective) coating was determined to provide adequate protection against AO degradation of the composite material and provided fiber containment, thus the structural integrity of the flexible batten was maintained. Both silicone coated and uncoated flexible battens maintained load carrying capabilities. Results of the testing did indicate that the CV1144 silicone protective coating was oxidized by AO reactions to form a brittle glassy (SiO2) skin that formed cracking patterns on all sides of the coated samples. The cracking was observed in samples that were mechanically stressed as well as samples in non-stressed conditions. The oxidized silicon was observed to randomly spall in small localized areas, on the flexible battens that underwent retraction-deployment cycling. Some darkening of the silicon, attributed to vacuum ultraviolet (VUV) radiation, was observed.

Stidham, Curtis R.↗
NASA NTRS2002

Effect of Random Geometric Uncertainty on the Computational Design of a 3-D Flexible Wing

The effect of geometric uncertainty due to statistically independent, random, normally distributed shape parameters is demonstrated in the computational design of a 3-D flexible wing. A first-order second-moment statistical approximation method is used to propagate the assumed input uncertainty through coupled Euler CFD aerodynamic / finite element structural codes for both analysis and sensitivity analysis. First-order sensitivity derivatives obtained by automatic differentiation are used in the input uncertainty propagation. These propagated uncertainties are then used to perform a robust design of a simple 3-D flexible wing at supercritical flow conditions. The effect of the random input uncertainties is shown by comparison with conventional deterministic design results. Sample results are shown for wing planform, airfoil section, and structural sizing variables.

Gumbert, C. R.↗
NASA NTRS2010

Robustness-Based Design Optimization Under Data Uncertainty

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Zaman, Kais↗
DOE OSTI2019

The combined force field-sampling problem in simulations of disordered amyloid-β peptides

Molecular dynamics simulations of intrinsically disordered proteins (IDPs) can provide high resolution structural ensembles if the force field is accurate enough and if the simulation sufficiently samples the conformational space of the IDP with the correct weighting of sub-populations. Here, we investigate the combined force field–sampling problem by testing a standard force field as well as newer fixed charge force fields, the latter specifically motivated for better description of unfolded states and IDPs, and comparing them with a standard temperature replica exchange (TREx) protocol and a non-equilibrium Temperature Cool Walking (TCW) sampling algorithm. The force field and sampling combinations are used to characterize the structural ensembles of the amyloid-beta peptides Aβ42 and Aβ43, which both should be random coils as shown recently by experimental nuclear magnetic resonance (NMR) and 2D Förster resonance energy transfer (FRET) experiments. The results illustrate the key importance of the sampling algorithm: while the standard force field using TREx is in poor agreement with the NMR J-coupling and nuclear Overhauser effect and 2D FRET data, when using the TCW method, the standard and optimized protein-water force field combinations are in very good agreement with the same experimental data since the TCW sampling method produces qualitatively different ensembles than TREx. Lastly, we also discuss the relative merit of the 2D FRET data when validating structural ensembles using the different force fields and sampling protocols investigated in this work for small IDPs such as the Aβ42 and Aβ43 peptides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter

Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to properly sample the high-probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of random noise that widens the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. A localized variant of the proposed method is developed. Numerical experiments with the Lorenz '63 model show that modified filters significantly improve the analyses for low dynamical ensemble sizes. Furthermore, localization experiments with the Lorenz '96 model show that the proposed methodology is extendable to larger systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons

Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli , Phaeobacter inhibens , Pseudomonas stutzeri , Shewanella amazonensis , and Shewanella oneidensis . To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens , we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative D-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are too laborious to be applied to hundreds of experimental conditions across multiple bacteria. Here, we describe an approach, random bar code transposon-site sequencing (RB-TnSeq), which greatly simplifies the measurement of gene fitness by using bar code sequencing (BarSeq) to monitor the abundance of mutants. We performed 387 genome-wide fitness assays across five bacteria and identified phenotypes for over 5,000 genes. RB-TnSeq can be applied to diverse bacteria and is a powerful tool to annotate uncharacterized genes using phenotype data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Uncertainty quantification in multiscale simulation of woven fiber composites

We report woven fiber composites have been increasingly employed as light-weight materials in aerospace, construction, and transportation industries due to their superior properties. These materials possess a hierarchical structure that necessitates the use of multiscale simulations in their modeling. To account for the inherent uncertainty in materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the significant computational costs and complexities in modeling spatially correlated structural variations coupled at different scales. In this work, a non-intrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model non-stationary and continuous (but not differentiable) spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF. We employ multi-response Gaussian RFs in top-down sampling and leverage statistical techniques (such as metamodeling and dimensionality reduction) to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven composite due to spatial variations of yarn angle, fiber volume fraction, and fiber misalignment angle. Finally, our results indicate that, even in linear analysis, the effect of uncertainty sources on the material’s response could be significant.

42 ENGINEERING↗
NASA NTRS1981

ECC ozonesonde performance at high altitudes: Pump efficiency

The ECC ozonesonde sampling behavior was examined at pressures ranging from 60 to 6 hPa with the objective of evaluating uncertainties in high altitude ozone data caused by variations in pumping efficiency. The averaged pump efficiency correction curve for a 43 sample set of 3A type ECC pumps showed a 2-3% bias from the curve provided by the manufacturer. In addition, random pump to pump variations (2 sigma) + or - were 5% at 6 hPa. These values probably represent minimum errors since the pumps were from the same production batch. A seven sample set of the newer 4A type ECC pumps was examined, with similar findings as for the 3A types.

Torres, A. L.↗