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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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All sources NASA NTRS DOE OSTI

NASA NTRS1993

SAVS: A Space Analysis and Visualization System

We propose to develop, test, demonstrate, and deliver to NASA a powerful and versatile data acquisition, manipulation, analysis and visualization system which will enhance scientific capabilities in the display and interpretation of diverse and distributed data within an integrated user-friendly environment. Our approach exploits existing technologies and combines three major elements into an easy-to-use interactive package: (1) innovative visualization software; (2) advanced database techniques; and (3) a rich set of mathematical and image processing tools. Visualization capabilities will include one-, two-, and three-dimensinal displays, along with animation, compression, warping and slicing functions. Analysis tools will include generic mathematical and statistical techniques along with the ability to use large scale models for interactive interpretation of large volume data sets. Our system will be implemented on Sun and EC UNIX workstations and on the Stardent Graphics Supercomputer. Our final deliverable will include complete documentation and a NASA/NSF-CDAW/SUNDIAL campaign demonstration.

Szuszczewicz, Edward P.↗

DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part I - Analysis with a Small Sample Size

Computer simulations, especially of complex phenomena, can be expensive, requiring high-performance computing resources. Often, to understand a phenomenon, multiple simulations are run, each with a different set of simulation input parameters. These data are then used to create an interpolant, or surrogate, relating the simulation outputs to the corresponding inputs. When the inputs and outputs are scalars, a simple machine learning model can suffice. However, when the simulation outputs are vector valued, available at locations in two or three spatial dimensions, often with a temporal component, creating a surrogate is more challenging. In this report, we use a two-dimensional problem of a jet interacting with high explosives to understand how we can build high-quality surrogates. The characteristics of our data set are unique - the vector-valued outputs from each simulation are available at over two million spatial locations; each simulation is run for a relatively small number of time steps; the size of the computational domain varies with each simulation; and resource constraints limit the number of simulations we can run. We show how we analyze these extremely large data-sets, set the parameters for the algorithms used in the analysis, and use simple ways to improve the accuracy of the spatio-temporal surrogates without substantially increasing the number of simulations required.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1977

A multiscale numerical study into the cascade of kinetic energy leading to severe local storms

The cascade of kinetic energy from macro- through mesoscales is studied on the basis of a nested grid system used to solve a set of nonlinear differential equations. The kinetic energy cascade and the concentration of vorticity through the hydrodynamic spectrum provide a means for predicting the location and intensity of severe weather from large-scale data sets. A mechanism described by the surface pressure tendency equation proves to be important in explaining how initial middle-tropospheric mass-momentum imbalances alter the low-level pressure field.

Paine, D. A.↗

NASA NTRS2010

Statistical Analysis of a Large Sample Size Pyroshock Test Data Set Including Post Flight Data Assessment

The Earth Observing System (EOS) Terra spacecraft was launched on an Atlas IIAS launch vehicle on its mission to observe planet Earth in late 1999. Prior to launch, the new design of the spacecraft's pyroshock separation system was characterized by a series of 13 separation ground tests. The analysis methods used to evaluate this unusually large amount of shock data will be discussed in this paper, with particular emphasis on population distributions and finding statistically significant families of data, leading to an overall shock separation interface level. The wealth of ground test data also allowed a derivation of a Mission Assurance level for the flight. All of the flight shock measurements were below the EOS Terra Mission Assurance level thus contributing to the overall success of the EOS Terra mission. The effectiveness of the statistical methodology for characterizing the shock interface level and for developing a flight Mission Assurance level from a large sample size of shock data is demonstrated in this paper.

Hughes, William O.↗

DOE OSTI2020

Metabolic Pathway Engineering

Modern microbial and enzyme engineering and their advancement are increasingly dependent on the marriage of a wide range of sophisticated technologies. For students entering the field of biotechnology, the outlook is indeed daunting. Expertise at levels beyond that of simple familiarity will be needed to conduct competitive research. It goes without saying that to be competitive, all new researchers in this field will need basic preparation in molecular biology, biochemistry, and genetics. Further, experience working with concepts and experimental tools in enzyme biochemistry and kinetics, gene editing, computational metabolic pathway modeling, experimental pathway flux analysis, and computational clustering tools to process complex data sets will be vital for success. We speculate that most biotechnology researchers in early career at this time will build teams of collaborators to address these disparate science fields rather than attempt to become experts in one lab.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗

NASA NTRS1993

Automated Analysis of a Large-Scale Sky Survey: The SKICAT System

We describe the application of decision tree based classification techniques to the development of an automated tool for the reduction of a large scientific data set. The primary benefits of the SKICAT approach are increased data reduction throughput, repeatability, and consistency of classification.

data analysis image databases↗

DOE OSTI2020

Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from 1000 pressure signals recorded at local ranges (<20 km) with scaled distance up to 8000 m/kg 1/3 . Additionally, the measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

58 GEOSCIENCES↗

DOE OSTI2020

Cosmological constraints from higher redshift gamma-ray burst, H ii starburst galaxy, and quasar (and other) data

ABSTRACT We use higher redshift gamma-ray burst (GRB), H ii starburst galaxy (H iiG), and quasar angular size (QSO-AS) measurements to constrain six spatially flat and non-flat cosmological models. These three sets of cosmological constraints are mutually consistent. Cosmological constraints from a joint analysis of these data sets are largely consistent with currently accelerating cosmological expansion and with cosmological constraints derived from a combined analysis of Hubble parameter (H(z)) and baryon acoustic oscillation (BAO, with Planck-determined baryonic matter density) measurements. A joint analysis of the H(z) + BAO + QSO-AS + H iiG + GRB data provides fairly model-independent determinations of the non-relativistic matter density parameter $\Omega _{\rm m_0}=0.313\pm 0.013$ and the Hubble constant $H_0=69.3\pm 1.2\, \rm {km \, s^{-1} \, Mpc^{-1}}$. These data are consistent with the dark energy being a cosmological constant and with spatial hypersurfaces being flat, but they do not rule out mild dark energy dynamics or a little spatial curvature. We also investigate the effect of including quasar flux measurements in the mix and find no novel conclusions.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2001

Invited review: gender issues related to spaceflight: a NASA perspective

This minireview provides an overview of known and potential gender differences in physiological responses to spaceflight. The paper covers cardiovascular and exercise physiology, barophysiology and decompression sickness, renal stone risk, immunology, neurovestibular and sensorimotor function, nutrition, pharmacotherapeutics, and reproduction. Potential health and functional impacts associated with the various physiological changes during spaceflight are discussed, and areas needing additional research are highlighted. Historically, studies of physiological responses to microgravity have not been aimed at examining gender-specific differences in the astronaut population. Insufficient data exist in most of the discipline areas at this time to draw valid conclusions about gender-specific differences in astronauts, in part due to the small ratio of women to men. The only astronaut health issue for which a large enough data set exists to allow valid conclusions to be drawn about gender differences is orthostatic intolerance following shuttle missions, in which women have a significantly higher incidence of presyncope during stand tests than do men. The most common observation across disciplines is that individual differences in physiological responses within genders are usually as large as, or larger than, differences between genders. Individual characteristics usually outweigh gender differences per se.

NASA Discipline General Space Life Sciences↗

DOE OSTI2026

Accelerating Structure–Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting data set diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral data sets. Dual-VAE embeds local structures and spectroscopic responses into a shared latent manifold that serves as a structure–property relationship map. We applied this framework for the investigation of halide perovskite films by using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress the charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials.

atomic force microscopy↗

NASA NTRS1994

Measurement of HO2 and other trace gases in the stratosphere using a high resolution far-infrared spectrometer

This report covers the time period 1 Jul. to 31 Dec. 1993. There were no balloon or airplane flights during this reporting period, instead we concentrated on analyzing our existing data. This was facilitated by a recently completed program of enhancements made in our data reduction software. We are using our data sets to examine the changes in stratospheric chemistry over a variety of time scales. Ongoing projects include investigating the diurnal variation of OH, HO2, and H2O2 and exploring their relationships with other simultaneously measured species; measuring long term trends in HF and HCl; and looking for changes caused by the June 1991 Pinatubo eruption. We are also continuing to analyze the large set of data collected during the AASE 2.

Traub, Wesley A.↗

NASA NTRS1993

Status of LDEF ionizing radiation measurements and analysis

At this symposium significant new data and analyses were reported in cosmic ray research, radiation dosimetry, induced radioactivity, and radiation environment modeling. Measurements of induced radioactivity and absorbed dose are nearly complete, but much analysis and modeling remains. Measurements and analyses of passive nuclear track detectors (PNTD), used to derive the cosmic ray composition and spectra, and linear energy transfer (LET) spectra, are only a few percent complete, but important results have already emerged. As one might expect at this stage of the research, some of the new information has produced questions rather than answers. Low-energy heavy nuclei detected by two experiments are not compatible with known solar or cosmic components. Various data sets on absorbed dose are not consistent, and a new trapped proton environment model does not match the absorbed dose data. A search for cosmogenic nuclei other than Be-7 on Long Duration Exposure Facility (LDEF) surfaces has produced an unexpected result, and some activation data relating to neutrons is not yet understood. Most of these issues will be resolved by the analysis of further experiment data, calibrations, or the application of the large LDEF data set that offers alternate data or analysis techniques bearing on the same problem. The scope of the papers at this symposium defy a compact technical summary. I have attempted to group the new information that I noted into the following groups: induced radioactivity; absorbed dose measurements; LET spectra and heavy ion dosimetry; environment modeling and three dimensional shielding effects; cosmogenic nuclei; and cosmic rays and other heavy ions. The papers generally are expository and have excellent illustrations, and I refer to their figures rather than reproduce them here. The general program and objectives of ionizing radiation measurements and analyses on LDEF has been described previously.

Parnell, Thomas A.↗

DOE OSTI2020

Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach

In recent years there has been a considerable drive towards data-driven analysis, discovery and control of dynamical systems. To this end, operator theoretic methods, namely, Koopman operator methods have gained a lot of interest. In general, the Koopman operator is obtained as a solution to a least-squares problem, and as such, the Koopman operator can be expressed as a closed-form solution that involves the computation of Moore-Penrose inverse of a matrix. For high dimensional systems and also if the size of the obtained data-set is large, the computation of the Moore-Penrose inverse becomes computationally challenging. In this paper, we provide an algorithm for computing the Koopman operator for high dimensional systems in a time-efficient manner. We further demonstrate the efficacy of the proposed approach on two different systems, namely a network of coupled oscillators (with state-space dimension up to 2500) and IEEE 68 bus system (with state-space dimension 204 and up to 24,000 time-points).

Sinha, Subhrajit↗

NASA NTRS1991

Studies of tropical ocean dynamics using the TOPEX/Poseidon altimeter-derived sea surface topography

Our primary objective is to carry out studies of tropical ocean dynamics using the TOPEX/POSEIDON altimeter-derived sea level data set in conjunction with the large in situ sea level data set available at the Tropical Ocean Global Atmosphere (TOGA) Sea Level Center. These studies will be supported by an interaction with numerical model simulations of tropical ocean variability. Five specific objectives can be identified: (1) research products; (2) short-term climate variations; (3) tropical-current variations; (4) synoptic-scale variability; and (5) model simulations.

Lukas, Roger↗

NASA NTRS2014

Jet-Surface Interaction Test: Flow Measurements Results

Modern aircraft design often puts the engine exhaust in close proximity to the airframe surfaces. Aircraft noise prediction tools must continue to develop in order to meet the challenges these aircraft present. The Jet-Surface Interaction Tests have been conducted to provide a comprehensive quality set of experimental data suitable for development and validation of these exhaust noise prediction methods. Flow measurements have been acquired using streamwise and cross-stream particle image velocimetry (PIV) and fluctuating surface pressure data acquired using flush mounted pressure transducers near the surface trailing edge. These data combined with previously reported far-field and phased array noise measurements represent the first step toward the experimental data base. These flow data are particularly applicable to development of noise prediction methods which rely on computational fluid dynamics to uncover the flow physics. A representative sample of the large flow data set acquired is presented here to show how a surface near a jet affects the turbulent kinetic energy in the plume, the spatial relationship between the jet plume and surface needed to generate surface trailing-edge noise, and differences between heated and unheated jet flows with respect to surfaces.

Flow↗

NASA NTRS2019

Python & Qt, Powerful Tools for Technical Computing

The objective of this presentation is to give a brief overview of Python computer language and Qt for Python which provides an interface to Python for building graphical applications. The Qt language provides a method for rapid programming of Graphical User Interfaces (GUIs) that are highly scalable, robust and platform independent. Both Python and Qt provide a powerful set of tools for Dynamic Analysis which are based on Open-Source software. Many problems and calculations in Structural Dynamics such as Power Spectral Density, Shock Response Spectrum and Vibration Response Spectrum can be easily calculated using these tools. The advantage of using Open-Source software is the ability to create custom graphical user interfaces similar to Matlab without the expense of software licensing and the ability to customize the software to an organization's specific needs. Also, another advantage is the ability to know which algorithms are being used by the GUI, know the numerical limitations and scale to large size data sets. I will end the presentation by demonstrating a Structural Dynamics GUI I created that was designed primarily to interactively analyze Post Flight high speed data provided by the ground station telemetry networks.

Grillo, Vincent↗

NASA NTRS2001

Efficiency of parallel direct optimization

Tremendous progress has been made at the level of sequential computation in phylogenetics. However, little attention has been paid to parallel computation. Parallel computing is particularly suited to phylogenetics because of the many ways large computational problems can be broken into parts that can be analyzed concurrently. In this paper, we investigate the scaling factors and efficiency of random addition and tree refinement strategies using the direct optimization software, POY, on a small (10 slave processors) and a large (256 slave processors) cluster of networked PCs running LINUX. These algorithms were tested on several data sets composed of DNA and morphology ranging from 40 to 500 taxa. Various algorithms in POY show fundamentally different properties within and between clusters. All algorithms are efficient on the small cluster for the 40-taxon data set. On the large cluster, multibuilding exhibits excellent parallel efficiency, whereas parallel building is inefficient. These results are independent of data set size. Branch swapping in parallel shows excellent speed-up for 16 slave processors on the large cluster. However, there is no appreciable speed-up for branch swapping with the further addition of slave processors (>16). This result is independent of data set size. Ratcheting in parallel is efficient with the addition of up to 32 processors in the large cluster. This result is independent of data set size. c2001 The Willi Hennig Society.

NASA Discipline Evolutionary Biology↗

DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗