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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Modernization of B-2 Data, Video, and Control Systems Infrastructure

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Spacecraft Propulsion Research Facility, commonly referred to as B-2, is NASA's third largest thermal-vacuum facility with propellant systems capability. B-2 has completed a modernization effort of its facility legacy data, video and control systems infrastructure to accommodate modern integrated testing and Information Technology (IT) Security requirements. Integrated systems tests have been conducted to demonstrate the new data, video and control systems functionality and capability. Discrete analog signal conditioners have been replaced by new programmable, signal processing hardware that is integrated with the data system. This integration supports automated calibration and verification of the analog subsystem. Modern measurement systems analysis (MSA) tools are being developed to help verify system health and measurement integrity. Legacy hard wired digital data systems have been replaced by distributed Fibre Channel (FC) network connected digitizers where high speed sampling rates have increased to 256,000 samples per second. Several analog video cameras have been replaced by digital image and storage systems. Hard-wired analog control systems have been replaced by Programmable Logic Controllers (PLC), fiber optic networks (FON) infrastructure and human machine interface (HMI) operator screens. New modern IT Security procedures and schemes have been employed to control data access and process control flows. Due to the nature of testing possible at B-2, flexibility and configurability of systems has been central to the architecture during modernization.

Cmar, Mark D.↗
DOE OSTI2012

A DATA-DRIVEN MODEL FOR SPECTRA: FINDING DOUBLE REDSHIFTS IN THE SLOAN DIGITAL SKY SURVEY

We present a data-driven method-heteroscedastic matrix factorization, a kind of probabilistic factor analysis-for modeling or performing dimensionality reduction on observed spectra or other high-dimensional data with known but non-uniform observational uncertainties. The method uses an iterative inverse-variance-weighted least-squares minimization procedure to generate a best set of basis functions. The method is similar to principal components analysis (PCA), but with the substantial advantage that it uses measurement uncertainties in a responsible way and accounts naturally for poorly measured and missing data; it models the variance in the noise-deconvolved data space. A regularization can be applied, in the form of a smoothness prior (inspired by Gaussian processes) or a non-negative constraint, without making the method prohibitively slow. Because the method optimizes a justified scalar (related to the likelihood), the basis provides a better fit to the data in a probabilistic sense than any PCA basis. We test the method on Sloan Digital Sky Survey (SDSS) spectra, concentrating on spectra known to contain two redshift components: these are spectra of gravitational lens candidates and massive black hole binaries. We apply a hypothesis test to compare one-redshift and two-redshift models for these spectra, utilizing the data-driven model trained on a random subset of all SDSS spectra. This test confirms 129 of the 131 lens candidates in our sample and all of the known binary candidates, and turns up very few false positives.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling

Global Sensitivity Analysis (GSA) aims to understand the relative importance of uncertain input variables to model response. Conventional GSA involves calculating sensitivity (Sobol’) indices for a model with known model parameter distributions. However, model parameters are affected by aleatory and epistemic uncertainty, with the latter often caused by lack of data. In this paper, we propose a new framework to quantify uncertainty in probability model-form and model parameters resulting from small datasets and integrate these uncertainties into Sobol’ index estimates. First, the process establishes, through Bayesian multimodel inference, a set of candidate probability models and their associated probabilities. Imprecise Sobol’ indices are calculated from these probability models using an importance sampling reweighting approach. This results in probabilistic Sobol’ indices, whose distribution characterizes uncertainty in the sensitivity resulting from small dataset size. The imprecise Sobol’ indices thus provide a measure of confidence in the sensitivity estimate and, moreover, can be used to inform data collection efforts targeted to minimize the impact of uncertainties. Through an example studying the parameters of a Timoshenko beam, we show that these probabilistic Sobol’ indices converge to the true/deterministic Sobol’ indices as the dataset size increases and hence, distribution-form uncertainty reduces. The approach is then applied to assess the sensitivity of the out-of-plane properties of an E-glass fiber composite material to its constituent properties. This second example illustrates the approach for an important class of materials with wide-ranging applications when data may be lacking for some input parameters.

42 ENGINEERING↗
DOE OSTI2014

Large Deviation Methods for the Analysis and Design of Monte Carlo Schemes in Physics and Chemistry. Final Technical Report

This proposal is concerned with applications of Monte Carlo to problems in physics and chemistry where rare events degrade the performance of standard Monte Carlo. One class of problems is concerned with computation of various aspects of the equilibrium behavior of some Markov process via time averages. The problem to be overcome is that rare events interfere with the efficient sampling of all relevant parts of phase space. A second class concerns sampling transitions between two or more stable attractors. Here, rare events do not interfere with the sampling of all relevant parts of phase space, but make Monte Carlo inefficient because of the very large number of samples required to obtain variance comparable to the quantity estimated. The project uses large deviation methods for the mathematical analyses of various Monte Carlo techniques, and in particular for algorithmic analysis and design. This is done in the context of relevant application areas, mainly from chemistry and biology.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data, and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Gebre↗
DOE OSTI2013

Sources of Technical Variability in Quantitative LC-MS Proteomics: Human Brain Tissue Sample Analysis.

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE clean-up (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its’ suitability for discovery proteomics studies is demonstrated.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

Quantitative Mapping of Nanoscale Chemical Dynamics in Sub-sampled Operando (S)TEM Images Using Spatio-Temporal Analytics

One of the main limitations in the use of operando scanning transmission electron microscopes to study dynamic chemical processes is the effect of the electron beam on the kinetics of the reaction being observed. Here we demonstrate that a flexible Gaussian mixture model can be used to extract quantitative information directly from sub-sampled images, i.e.~images where not all the pixels in the image are illuminated with the beam. The use of this method is demonstrated for the charge/discharge cycle of a Li-battery, where the deposition and dissolution of Li at the anode can be accurately quantified for sub-sampling levels down to 1%. Performing operando imaging using a small fraction of the pixels means that the observations can significantly reduce the effect of the beam, automatically increase the imaging speed and decrease the total data transfer rate required. Such new software capabilities offer the potential to significantly widen the application of operando hardware approaches to study nanoscale dynamics in materials.

Stanfill, Bryan A.↗
DOE OSTI2019

Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel

Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.

Sainju, Rajat↗
DOE OSTI2017

A Piecewise Local Partial Least Squares (PLS) Method for the Quantitative Analysis of Plutonium Nitrate Solutions

We have developed a piecewise local (PL) partial least squares (PLS) analysis method for total plutonium measurements by absorption spectroscopy in nitric acid-based nuclear material processing streams. Instead of using a single PLS model that covers all expected solution conditions, the method selects one of several local models based on an assessment of solution absorbance, acidity, and Pu oxidation state distribution. The local models match the global model for accuracy against the calibration set, but were observed in several instances to be more robust to variations associated with measurements in the process. The improvements are attributed to the relative parsimony of the local models. Not all of the sources of spectral variation are uniformly present at each part of the calibration range. Thus, the global model is locally overfitting and susceptible to increased variance when presented with new samples. A second set of models quantifies the relative concentrations of Pu(III), (IV), and (VI). Standards containing a mixture of these species were not at equilibrium due to a disproportionation reaction. Therefore, a separate principal component analysis is used to estimate of the concentrations of the individual oxidation states in these standards in the absence of independent confirmatory analysis. The PL analysis approach is generalizable to other systems where the analysis of chemically complicated systems can be aided by rational division of the overall range of solution conditions into simpler sub-regions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Improved Quantitative Dynamic Range of Time-of-Flight Mass Spectrometry by Simultaneously Waveform-Averaging and Ion-Counting Data Acquisition

Two different types of data acquisition methods, “averaging mode” and “ion-counting mode”, have been used in a time-of-flight (TOF) mass spectrometry. The most common method is an averaging mode that sums waveform signals obtained from each flight cycle. While it is possible to process many ions arriving at the same TOF in one flight cycle, low-abundance ions are difficult to measure because ion signals are overwhelmed by noises from the detection system. An ion-counting mode is suitable for the detection of such low-concentration ions, but counting loss occurs when two or more ions arrive at the detector within the dead time of the acquisition system. In this study, we introduce a technique that combines two methods to measure target ions with a high concentration difference, i.e., averaging mode and ion-counting mode are used simultaneously for high abundant and trace ions, respectively. By processing waveforms concurrently during data acquisition, one can choose to analyze either or both types of data to achieve a highly quantitative mass spectrum over a wide range of sample concentrations. The result of the argon isotope analysis shows that this method provides a more accurate determination of the isotope ratio compared to averaging mode alone at one-twentieth of the analysis time required by ion-counting alone. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Identification of blind geothermal resources in Surprise Valley, CA, using publicly available groundwater well water quality data

Geothermal resource exploration is generally limited to areas with surface expressions of thermal activity (fumaroles and hot springs), or relies on expensive geophysical exploration techniques. The hidden subsurface distribution of geothermal fluids has been identified using a free and publicly available water quality dataset from agricultural and domestic water wells in Surprise Valley, northeastern California. Thermally evolved waters in Surprise Valley have element ratios that vary in response to Ca carbonate and Mg silicate mineral precipitation, and have elevated total dissolved solids (TDS). The arid climate in Surprise Valley leads to surface water evaporation in a closed basin, producing high TDS Na-Cl-CO 3 -SO 4 brines in three ephemeral alkali lakes and in shallow groundwater under elevated soil CO 2 conditions. Evaporated fluids in Surprise Valley follow a chemical divide that leads to Ca carbonate and Mg silicate mineral precipitation. Plots of dissolved element ratios can be used to distinguish groundwater affected by evaporation from water affected by thermal water-rock interaction, however it is challenging to select components for plotting that best illustrate different fluid evolution mechanisms. Here, we use a principal component analysis of centered log-ratio transformed data, coupled with geochemical models of fluid evaporation and thermal mixing pathways, to identify components to plot that distinguish between groundwater samples influenced by evaporation from those influenced by thermal processes. We find that groundwater samples with a thermal signature come from wells that define a coherent, linear geographical distribution that closely matches the location of known and inferred faults. Finally, modification of the general approach employed here provides promise for identifying blind geothermal resources in other locations, by applying low-cost geochemical modeling and statistical techniques to areas where large groundwater quality geochemical datasets are available.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Genomic Evolution of Breast Cancer Metastasis and Relapse

Patterns of genomic evolution between primary and metastatic breast cancer have not been studied in large numbers, despite patients with metastatic breast cancer having dismal survival. We sequenced whole genomes or a panel of 365 genes on 299 samples from 170 patients with locally relapsed or metastatic breast cancer. Several lines of analysis indicate that clones seeding metastasis or relapse disseminate late from primary tumors, but continue to acquire mutations, mostly accessing the same mutational processes active in the primary tumor. Most distant metastases acquired driver mutations not seen in the primary tumor, drawing from a wider repertoire of cancer genes than early drivers. Lastly, these include a number of clinically actionable alterations and mutations inactivating SWI-SNF and JAK2-STAT3 pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Ultrasonic additive manufacturing of steel: Method, post-processing treatments and properties

In this study, ultrasonic additive manufacturing (UAM) was applied to fabricate laminated carbon steel structures. The feasibility of UAM to manufacture low-alloy carbon steel samples was proven. Interface investigation of the UAM parts was conducted by SEM, EBSD and TEM analysis. Multiple defects at the interfaces of the as-printed parts were observed. In order to improve the structural homogeneity and mechanical properties of the parts, spark plasma sintering (SPS) and hot isostatic pressing (HIP) post-treatments were applied. As a result, after both treatments, interface defects were healed and the shear strength of the welding interfaces was significantly improved. Lastly, treatment by SPS apparatus showed higher shear strength improvements as compared to the HIP-treated specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Review of multi-faceted morphologic signatures of actinide process materials for nuclear forensic science

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. In conclusion, this review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Analysis of heterogeneities in strain and microstructure in aluminum alloy and magnesium processed by high-pressure torsion

Two distinct bulk light metals were opted to study the shear strain evolution and associated heterogeneities in texture/microstructure development during torsional straining by high pressure torsion (HPT): a face centered cubic Al alloy (A5086) and a hexagonal commercial purity Mg. Relatively thick disk samples - four times thicker than usually employed in HPT process - were processed to 180° and 270° rotations. With the help of X-ray tomography, the shear strain gradients were examined in the axial direction. The results showed strongly localized shear deformation in the middle plane of the disks in both materials. These gradients involved strong heterogeneities in texture, microstructure and associated hardness, in particular through the thickness direction at the periphery of the disk where the interplay between significant strain hardening and possible dynamic recrystallization could occur. - Highlights: •HPT processing was conducted on bulk specimens thicker than the usual thin-disks. •The Al alloy (A5086) and commercial purity magnesium samples were compared. •Distributions of strain and microhardness were evaluated in the radial and axial direction. •Plastic deformation is highly localized in the middle plane at outer edge in both materials. •Different DRX rates governed the differences in microstructure and hardening behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Structure of graphene oxide dispersed with ZnO nanoparticles

Graphene has been proposed as a promising two-dimensional nanomaterial with outstanding electronic, optical, thermal and mechanical properties for many applications. In present work a process of dispersion of graphene oxide with ZnO nanoparticles in ethanol solution with different pH values, have been studied. Samples have been characterized by XRD, SEM, PL, UV-visible spectroscopy and particles size measurement. The results analysis indicates overall improved emission spectrum. It has been observed that the average diameter of RGO (Reduced Graphene Oxide) decreases in presence of ZnO nanoparticles from 3.8μm to 0.41μm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

A multi-probe thermophoretic soot sampling system for high-pressure diffusion flames

Optical diagnostics and physical probing of the soot processes in high pressure combustion pose challenges that are not faced in atmospheric flames. One of the preferred methods of studying soot in atmospheric flames is in situ thermophoretic sampling followed by transmission electron microscopy imaging and analysis for soot sizing and morphology. The application of this method of sampling to high pressures has been held back by various operational and mechanical problems. In this work, we describe a rotating disk multi-probe thermophoretic soot sampling system, driven by a microstepping stepper motor, fitted into a high-pressure chamber capable of producing sooting laminar diffusion flames up to 100 atm. Innovative aspects of the sampling system design include an easy and precise control of the sampling time down to 2.6 ms, avoidance of the drawbacks of the pneumatic drivers used in conventional thermophoretic sampling systems, and the capability to collect ten consecutive samples in a single experimental run. Proof of principle experiments were performed using this system in a laminar diffusion flame of methane, and primary soot diameter distributions at various pressures up to 10 atm were determined. High-speed images of the flame during thermophoretic sampling were recorded to assess the influence of probe intrusion on the flow field of the flame.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Luminescence evolution from alumina ceramic surface before flashover under direct and alternating current voltage in vacuum

The luminescence evolution phenomena from alumina ceramic surface in vacuum under high voltage of direct and alternating current are reported, with the voltage covering a large range from far below to close to the flashover voltage. Its time resolved and spatial distributed behaviors are examined by a photon counting system and an electron-multiplying charge-coupled device (EMCCD) together with a digital camera, respectively. The luminescence before flashover exhibits two stages as voltage increasing, i.e., under a relative low voltage (Stage A), the luminescence is ascribed to radiative recombination of hetero-charges injected into the sample surface layer by Schottky effect; under a higher voltage (Stage B), a stable secondary electron emission process, resulting from the Fowler-Nordheim emission at the cathode triple junction (CTJ), is responsible for the luminescence. Spectrum analysis implies that inner secondary electrons within the surface layer of alumina generated during the SSEE process also participate in the luminescence of Stage B. A comprehensive interpretation of the flashover process is formulated, which might promote a better understanding of flashover issue in vacuum.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗