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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Automated Lab-on-a-Chip Electrophoresis System

Capillary electrophoresis is an analytical technique that can be used to detect and quantify extremely small amounts of various biological molecules. In the search for biochemical traces of life on other planets, part of this search involves an examination of amino acids, which are the building blocks of life on Earth. The most sensitive method for detecting amino acids is the use of laser induced fluorescence. However, since amino acids do not, in general, fluoresce, they first must be reacted with a fluorescent dye label prior to analysis. After this process is completed, the liquid sample then must be transported into the electrophoresis system. If the system is to be reused multiple times, samples must be added and removed each time. In typical laboratories, this process is performed manually by skilled human operators using standard laboratory equipment. This level of human intervention is not possible if this technology is to be implemented on extraterrestrial targets. Microchip capillary electrophoresis (CE) combined with laser induced fluorescence detection (LIF) was selected as an extremely sensitive method to detect amino acids and other compounds that can be tagged with a fluorescent dye. It is highly desirable to package this technology into an integrated, autonomous, in situ instrument capable of performing CE-LIF on the surface of an extraterrestrial body. However, to be fully autonomous, the CE device must be able to perform a large number of sample preparation and analysis operations without the direct intervention of a human.

Willis, Peter A.↗
NASA NTRS2012

SOFIA: Flying the Telescope

The primary focus of this paper is how the flight test team for the Stratospheric Observatory For Infrared Astronomy (SOFIA) re-cast an extensive developmental test program to meet key milestones while simultaneously ensuring safe certification of the airframe and delivery of an operationally relevant platform, ultimately saving the overall program from financial demise. Following a brief introduction to the observatory and what it is designed to do, SOFIAs planned developmental test program is summarized, including analysis and design philosophy, envelope expansion, model validation and airframe certification. How NASA used lessons learned from other aircraft that employed open cavities in flight is explained as well as how and why the chosen design was selected. The approach to aerodynamic analysis, including bare airframe testing, wind tunnel testing, computational fluid dynamics and finite element modeling proved absolutely critical. Despite a solid analytical foundation, many unknowns remained. History provides several examples of disastrous effects on both systems and flight safety if cavity design is not approached properly. For these reasons, an extensive test plan was developed to ensure a safe and thorough build-up for envelope expansion, airframe certification and early science missions. Unfortunately, as is often the case, because of chronic delays in overall program execution, severe schedule and funding pressures were present. If critical milestones were not met, domestic as well as international funding was in serious jeopardy, and the demise of the entire program loomed large. Concentrating on rigorous model validation, the test team challenged certification requirements, increased test efficiency and streamlined engineering analysis. This resulted in the safe reduction of test point count by 72%, meeting all program milestones and a platform that soundly satisfied all operational science requirements. Results from early science missions are shown and a proof of concept mission for which SOFIA was opportunely positioned is showcased. Success on this time-critical mission to observe a rare astronomical event proved the usefulness of an airborne observatory and the value in waiting for the capability provided by SOFIA. Finally, lessons learned in the test program are presented with emphasis on how lessons from previous aircraft and successful test programs were applied to SOFIA. Effective application of these lessons was crucial to the success of the SOFIA flight test program. SOFIA is an international cooperative program between NASA and the German Space Agency, DLR. It is a 2.5 meter (100-inch) telescope mounted in a Boeing 747SP aircraft used for astronomical observations at altitudes above 35,000 feet. SOFIA will accommodate a host of scientific instruments from the international science community and has a planned operational lifespan of more than 20 years.

Asher, Troy A.↗
NASA NTRSDate not supplied

An Introduction to Data Visualization

Data visualization can be used to present findings, explore data, and use the human eye to find patterns that a computer would struggle to locate. Borrowing tools from art, storytelling, data analytics and software development, data visualization is an indispensable part of the analysis process. While data visualization usage spans across multiple disciplines and sectors, most never receive formal training in the subject. As such, this tutorial will introduce key data visualization building blocks and how to best use those building blocks for different scenarios and audiences. We will also go over tips on accessibility, design and interactive elements. While this will by no means be a complete overview of the data visualization field, by building a foundation and introducing some rules of thumb, attendees will be better equipped for communicating their findings to their audience.

data visualization↗
DOE OSTI2012

Oscillator Seeding of a High Gain Harmonic Generation FEL in a Radiator-First Configuration

A longitudinally coherent X-ray pulse from a high repetition rate free electron laser (FEL) is desired for a wide variety of experimental applications. However, generating such a pulse with a repetition rate greater than 1 MHz is a significant challenge. The desired high repetition rate sources, primarily high harmonic generation with intense lasers in gases or plasmas, do not exist now, and, for the multi-MHz bunch trains that superconducting accelerators can potentially produce, are likely not feasible with current technology. In this paper, we propose to place an oscillator downstream of a radiator. The oscillator generates radiation that is used as a seed for a high gain harmonic generation (HGHG) FEL which is upstream of the oscillator. For the first few pulses the oscillator builds up power and, until power is built up, the radiator has no HGHG seed. As power in the oscillator saturates, the HGHG is seeded and power is produced. The dynamics and stability of this radiator-first scheme is explored analytically and numerically. A single-pass map is derived using a semi-analytic model for FEL gain and saturation. Iteration of the map is shown to be in good agreement with simulations. A numerical example is presented for a soft X-ray FEL.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

MO-FG-303-02: BEST IN PHYSICS (THERAPY): Cherenkov Emission Dosimetry: Feasibility for Electron Radiotherapy

Purpose: To investigate from first principles, corroborated by Monte Carlo simulations and experimental measurements, the feasibility of developing a relative Cherenkov emission (CE) dosimetry protocol for electron beam radiotherapy. Methods: Monte Carlo (MC) simulations of mono-energetic electrons incident on water were carried out in Geant4. Percent depth Cherenkov emission (PDCE) and dose (PDD) distributions were scored for incidence energies of 4, 6, 9, 12, 15, and 18 MeV. PDCE-to-PDD analytical conversion models were developed from least-squares data fits generated for PDD as a function of PDCE at the same depth and at different depths. Experimental techniques for validation of these models are examined. Results: Same-depth PDD versus PDCE data fits indicate that although the relationship is linear to first order (correlation r > 0.9 for all energies), it is much more accurately approximated by separate linear and quadratic models for the build-up and drop-off regions, respectively (r > 0.999), which is theoretically underpinned. To understand the source of this relationship and its basis for developing robust conversion models, an approximate quadratic first-principles model was derived and found in agreement with MC/measured data (20% deviation at worst). Conversely, data fits of PDD versus different-depth PDCE unveiled a depth-invariant effective point of measurement of 1.5–2.1 mm downstream with 4–18 MeV incidence, respectively (r > 0.999 in the drop-off region). We present an analytical first-principles justification for this shift. This method led to errors of <1% in drop-off region PDD (<2% for PDD<20% with 4 MeV incidence) and <0.2 mm in practical range prediction. Conclusion: We present robust quantitative prediction models, derived from first-principles and supported by simulation and measurement, for relative dose from Cherenkov emission by high-energy electrons. This constitutes a major step towards development of protocols for routine clinical quality assurance as well as real-time in vivo Cherenkov dosimetry in radiotherapy. The authors acknowledge partial support by Fonds de recherche du Quebec - Nature et technologies (FRQNT), CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (NSERC), CREATE Integrated Sensor Systems grant of NSERC, the Canadian Institutes of Health Research (CIHR), and NSERC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Structural transition and recovery of Ge implanted β -Ga 2 O 3

Ion implantation-induced effects were studied in Ge implanted β-Ga 2 O 3 with the fluence and energy of 3 × 10 13 cm -2 /60 keV, 5 × 10 13 cm -2 /100 keV, and 7 × 10 13 cm -2 /200 keV using analytical electron microscopy via scanning/transmission electron microscopy, electron energy loss spectroscopy, and precession electron diffraction via TopSpin. Imaging shows an isolated band of damage after Ge implantation, which extends ~130 nm from the sample surface and corresponds to the projected range of the ions. Electron diffraction demonstrates that the entirety of the damage band is the κ phase, indicating an implantation-induced phase transition from β to κ-Ga 2 O 3 . Post-implantation annealing at 1150 °C for 60 s under the O 2 atmosphere led to a back transformation of κ to β; however, an ~17 nm damage zone remained at the sample surface. Despite the back transformation from κ to β with annealing, O K-edge spectra show changes in the fine structure between the pristine, implanted, and implanted-annealed samples, and topspin strain analysis shows a change in strain between the two samples. These data indicate differences in the electronic/chemical structure, where the change of the oxygen environment extended beyond the implantation zone (~130 nm) due to the diffusion of Ge into the bulk material, which, in turn, causes a tensile strain of 0.5%. This work provides a foundation for understanding of the effects of ion implantation on defect/phase evolution in β-Ga 2 O 3 and the related recovery mechanism, opening a window toward building a reliable device for targeted applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Aliphatic Amines from Waste Polyolefins by Tandem Pyrolysis, Hydroformylation, and Reductive Amination

Pyrolysis of waste plastics can produce a product mixture with a high concentration of olefins (>50 wt%). The olefins, as building blocks in the petroleum industry, are potential precursors for valuable commodity chemicals with higher values (>$2000 per ton). In this work, we produce aldehydes by hydroformylation of the olefins present in pyrolysis oil from colored post-consumer recycled high-density polyethylene (PCR-HDPE). The obtained aldehydes in the oil are then converted into aliphatic amines via reductive amination with a Ru/C catalyst. The aminated oil was characterized by multiple analytical chemistry techniques including elemental analysis, nuclear magnetic resonance spectroscopy, high-resolution liquid chromatography-mass spectrometry, and gas chromatography-mass spectrometry with a Polyarc flame ionization detector. The concentration of metals in the PCR-HDPE and oil changes during the tandem processes, showing limited effects of these elements (e.g., Al, Ca, Fe, Mg, Ti, Zn) on hydroformylation and reductive amination. Additionally, we demonstrated that reductive amination of aldehydes with varied carbon numbers and branching properties can be achieved in the presence of a complex mixture, including paraffins and aromatics. The results indicate that waste plastics have the potential to serve as a renewable source for mono and di aliphatic amines, thereby diminishing reliance on fossil feedstocks as the current primary amine source.

aliphatic amines↗
DOE OSTI2021

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Abstract The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( $$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Temperature effect on laser-induced breakdown spectroscopy spectra of molten and solid salts

Laser-induced breakdown spectroscopy (LIBS) has been investigated as a potential analytical tool to improve operations and safeguards for electrorefiners, such as those used in processing spent nuclear fuel. This study set out to better understand the effect of sample temperature and physical state on LIBS spectra of molten and solid salts by building calibration curves of cerium and assessing self-absorption, plasma temperature, electron density, and local thermal equilibrium (LTE). Samples were composed of a LiCl–KCl eutectic salt, an internal standard of MnCl2, and varying concentrations of CeCl3 (0.1, 0.3, 0.5, 0.8, and 1.0 wt.% Ce) under different temperatures (773, 723, 673, 623, and 573 K). Analysis of salts in their molten form is preferred as plasma plumes from molten samples experienced less self-absorption, less variability in plasma temperature, and higher clearance of the minimum electron density required for local thermal equilibrium. These differences are attributed to plasma dynamics as a result of phase changes. Spectral reproducibility was also better in the molten state due to sample homogeneity.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2020

DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions

Abstract Multiplexed assays allow functional testing of large synthetic libraries of genetic elements, but are limited by the designability, length, fidelity and scale of the input DNA. Here, we improve DropSynth, a low-cost, multiplexed method that builds gene libraries by compartmentalizing and assembling microarray-derived oligonucleotides in vortexed emulsions. By optimizing enzyme choice, adding enzymatic error correction and increasing scale, we show that DropSynth can build thousands of gene-length fragments at >20% fidelity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

An Adaptive Sparse-Grid High-Order Stochastic Collocation Method for Bayesian Inference in Groundwater Reactive Transport Modeling

Although Bayesian analysis has become vital to the quantification of prediction uncertainty in groundwater modeling, its application has been hindered due to the computational cost associated with numerous model executions needed for exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, we utilize a compactly supported higher-order hierar- chical basis to construct the surrogate system, resulting in a significant reduction in the number of computational simulations required. In addition, we use hierarchical surplus as an error indi- cator to determine adaptive sparse grids. This allows local refinement in the uncertain domain and/or anisotropic detection with respect to the random model parameters, which further improves computational efficiency. Finally, we incorporate a global optimization technique and propose an iterative algorithm for building the surrogate system for the PPDF with multiple significant modes. Once the surrogate system is determined, the PPDF can be evaluated by sampling the surrogate system directly with very little computational cost. The developed method is evaluated first using a simple analytical density function with multiple modes and then using two synthetic groundwater reactive transport models. The groundwater models represent different levels of complexity; the first example involves coupled linear reactions and the second example simulates nonlinear ura- nium surface complexation. The results show that the aSG-hSC is an effective and efficient tool for Bayesian inference in groundwater modeling in comparison with conventional MCMC sim- ulations. The computational efficiency is expected to be more beneficial to more computational expensive groundwater problems.

Zhang, Guannan↗
DOE OSTI2014

Hydrogen isotope exchange in a metal hydride tube

This report describes a model of the displacement of one hydrogen isotope within a metal hydride tube by a different isotope in the gas phase that is blown through the tube. The model incorporates only the most basic parameters to make a clear connection to the theory of open-tube gas chromatography, and to provide a simple description of how the behavior of the system scales with controllable parameters such as gas velocity and tube radius. A single tube can be seen as a building block for more complex architectures that provide higher molar flow rates or other advanced design goals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

MCNP ® Code Version 6.3.0 Build Guide

This is a build guide for the MCNP ® code, version 6.3.0, that expands upon the README.md included with the source code. It covers compilers, dependencies, building, testing, and installing the code in one of its supported configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

The Origin of the Elements

The world around us is made of atoms. Did you ever wonder where these atoms came from? How was the gold in our jewelry, the carbon in our bodies, and the iron in our cars made? In this lecture, we will trace the origin of a gold atom from the Big Bang to the present day, and beyond. You will learn how the elements were forged in the nuclear furnaces inside stars, and how, when they die, these massive stars spread the elements into space. You will learn about the origin of the building blocks of matter in the Big Bang, and we will speculate on the future of the atoms around us today.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Experimental, Computational, Theoretical and Analytical Investigation of Flow Boiling in Reduced Gravity

Two-phase thermal management systems are far superior to their single-phase counterparts because of their ability to capitalize on the coolant’s both sensible and latent heats, thereby yielding orders of magnitude higher heat transfer coefficients and smaller system footprints. A vital knowledge necessary for their implementation in future space systems is performance in microgravity. Long-duration microgravity experiments are necessary to obtain reliable databases, which would then be used to build reliable predictive tools. To achieve this goal, investigators at the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) and the NASA Glenn Research Center (NASA-GRC) have been collaborating towards the development of the Flow Boiling and Condensation Experiment (FBCE) and eventual execution onboard the International Space Station (ISS). FBCE has now matured to a point where it is ready for transport to the ISS, where first tests will be conducted using the Flow Boiling Module (FBM). In preparation for the ISS tests, a series of pre-launch Mission Sequence Tests (MSTs) was performed at GRC in Earth gravity with FBM mounted in a vertical upflow orientation using n-perfluorohexane as working fluid. The pre-launch tests included variations of flow rate, surface heat flux, inlet conditions, and both single-sided and double-sided wall heating. This presentation will summarize experimental results from these tests as well as both analytic and theoretical tools for prediction of two-phase heat transfer coefficient and critical heat flux (CHF). Also discussed will be an assessment of predictive accuracy of these tools against the experimental data.

Mission Sequence tests↗
DOE OSTI2019

Building complex event processing capability for intelligent environmental monitoring

Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams, and the best practices for datastream processing and predictive analytics are yet to be established. This work presents a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. We show that the service-based CEP engine is instrumental for enabling environmental intelligent monitoring systems to ingest heterogeneous datastreams with scalable performance. Our CEP framework requires minimal coding from the user and can be easily extended to other similar environmental monitoring applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Isotope ratio characteristics and sensitivity for uranium determinations using a liquid sampling-atmospheric pressure glow discharge ion source coupled to an Orbitrap mass analyzer

Abstract The continued development of the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma as an ion source for diverse, elemental/isotopic analysis applications continues. To this end, characterization of the capabilities in performing precise and accurate isotope ratio (IR) determinations is essential. Based on past experience with the Thermo Exactive Orbitrap mass analyzer, the LS-APGD was interfaced with this instrument for these tests. While the Orbitrap platform has demonstrated excellent mass resolution and accuracy in “organic” mass spectrometry (MS) applications, work using an Orbitrap for IR analysis is very sparse. These efforts build off previous work in this coupling, where the importance of a few of the LS-APGD discharge parameters and Orbitrap data acquisition methods on IR precision and accuracy were probed. Presented here are the results of a study that evaluated the analytical precision for natural uranium sample (assumed 235U/238U = 0.0072) determinations. The instrumental parameters evaluated include the number of microscans and scans making up a data acquisition set, uranium concentration/signal level, sample make-up, and Fourier transform digitization window. Ultimately, a precision of 0.41% relative standard deviation (RSD) can be achieved for a single determination, with a reproducibility of 1.63 %RSD over 10 separate analytical measurements. A preliminary study of matrix effects on IR measurements of U is presented, highlighting the importance of pre-mass selection before injection into the Orbitrap. The analytical system sensitivity is suggested with the ability to produce a calibration function having an R2 value of >0.99 over a range of 4 orders of magnitude of concentration (~1 – 1000 ng mL-1). These efforts demonstrate the very promising pairing of the LS-APGD ionization source and the Orbitrap, pointing as well to definitive paths forward to better utilize both components in high quality isotope ratio mass spectrometry (IRMS).

Environmental Molecular Sciences Laboratory↗