SEARCH · Search NASA
Results for “Using”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Denoising 400-kHz ?Postage-Stamp PIV? using Uncertainty Quantification.
Abstract not provided.
A methodology for small scale rural land use mapping in semi-arid developing countries using orbital imagery. 2: Capabilities of remote sensing techniques for land use surveys
There are no author-identified significant results in this report.
Fabrication of Nanoporous Alumina Ultrafiltration Membrane with Tunable Pore Size Using Block Copolymer Templates
Control over nanopore size and 3D structure is necessary to advance membrane performance in ubiquitous separation devices. Here, inorganic nanoporous membranes are fabricated by combining the assembly of cylinder-forming poly(styrene-block-methyl methacrylate) (PS-b-PMMA) block copolymer and sequential infiltration synthesis (SIS). A key advance relates to the use of PMMA majority block copolymer films and the optimization of thermal annealing temperature and substrate chemistry to achieve throughfilm vertical PS cylinders. The resulting morphology allows for direct fabrication of nanoporous AlOx by selective growth of Al2O3 in the PMMA matrix during the SIS process, followed by polymer removal using oxygen plasma. Control over the pore diameter is achieved by varying the number of Al2O3 growth cycles, leading to pore size reduction from 21 to 16 nm. 3D characterization, using scanning transmission electron microscopy tomography, reveals that the AlOx channels are continuous through the film and have a gradual increase in pore size with depth. Finally, the ultrafiltration performance of the fabricated AlOx membrane for protein separation as a function of protein size and charge is demonstrated.
Use of the ERD for administrative monitoring of Theta
Monitoring the state of an HPC cluster in a timely and accurate fashion is critical to most system administration functions. For many Cray users, the first step in monitoring is ingestion of log files. Unfortunately, log parsing is an inherently inefficient process, requiring multiple software components to read and write from files on disk. Cray's own utilities use a message bus, the Event Router Daemon (ERD), for a wide variety of purposes. At the Argonne Leadership Computing Facility (ALCF), we have begun to use this message bus for monitoring via a client library written in Go, allowing us to read in structured data directly from Cray's services and, in many instances, bypass log files entirely. In this paper, we will examine the implementation and utilization of this approach on our 4392 node XC40, Theta, as well as the overall benefits and drawbacks to using the ERD for real-time monitoring.
Use of a Lignin-Based Admixture for Tailoring the Rheological Properties of Mortars for 3D Printing: Preprint
Efforts toward decarbonizing construction materials and industrial processes related to cement and concrete can be aided via multifaceted approaches that target alternative admixtures as well as precision control of fabrication. Chemical admixtures for water reduction have played a crucial role in the development of advanced concrete mixtures. Newer biomass processing techniques developed for aviation fuel production from corn stover biomass produce a more reactive lignin byproduct that is suitable for chemical modifications to mimic the properties of polycarboxylate ether admixtures with a smaller carbon footprint. The present study examines the use of lignin-based water-reducing admixture in cement pastes and mortar mixtures for 3D printing. The experimental program explores the use of different dosages of lignin-based admixture to produce 3D-printed samples with appropriate extrudability and buildability. The rheological characterization was performed to determine the flow curve of various mixtures. Finally, the heat of hydration of cement pastes was monitored via isothermal calorimetry to assess the impact of lignin-based admixtures on the hydration process of cement. The results of this study indicate that the use of biomass by-products, such as lignin-based admixtures have great potential to effectively control the fresh-state properties of cement-based materials.
Towards High-Resolution Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry Coupled to Shear Force Microscopy
Constant mode ambient mass spectrometry imaging (MSI) of tissue sections with high lateral resolution of better than 10 μm was performed by combining shear force microscopy with nanospray desorption electrospray ionization (nano-DESI). Shear force microscopy enabled precise control of the distance between the sample and nano-DESI probe during MSI experiments and provided information on sample topography. Proof-of-concept experiments were performed using lung and brain tissue sections representing spongy and dense tissues, respectively. Topography images obtained using shear force microscopy were comparable to the results obtained using contact profilometry over the same region of the tissue section. Variations in tissue height were found to be dependent on the tissue type and were in the range of 0–5 μm for lung tissue and 0–3 μm for brain tissue sections. Ion images of phospholipids obtained in this study are in good agreement with literature data. Normalization of nano-DESI MSI images to the signal of the internal standard added to the extraction solvent allowed us to construct high-resolution ion images free of matrix effects. .
Hydroxamate column-based purification of zirconium-89 (89Zr) using an automated fluidic platform
Zirconium-89 (89Zr) is a long-lived (t1/2 = 78.4 h) positron-emitting isotope that is useful for positron emission tomography (PET) based diagnostic imaging using radiolabeled antibodies. Hydroxamate resin columns are predominantly used for the purification of 89Zr from cyclotron bombarded natY targets dissolved in strong HCl. 89Zr is conventionally eluted from the resin in 1 M oxalic acid (H2C2O4), a complexant that is conducive to follow-on binding of 89Zr through a transchelation process to the deferoxamine siderophore. In the present study, we determined that a lower concentration of H2C2O4 eluent (0.8 M) is adequate to efficiently remove 89Zr from a column containing 100 mg hydroxamate resin. As a result, less buffering agents are needed to be added to the 89Zr product fraction prior to labeling. A simple automated fluidic system prototype has been developed to perform the steps required for 89Zr purification using a hydroxamate resin column (column conditioning in HCl, Y target dissolution, dissolved target solution load onto column, column washes using HCl and water, and 89Zr elution). The system performance was evaluated using several cyclotron bombarded Y targets; 89Zr product fractions demonstrated excellent chemical recoveries from these targets, with 1.0 mL product volumes yielding 89±2% of the column elution peak activity and 84±2% of 89Zr recovered from the target (at EOB). These results compare favorably with previously published 89Zr product volumes and yields, despite the lower concentration of H2C2O4 eluent employed. Transchelation of resulting 89Zr product fractions was performed to assess product quality. The effective specific activity (ESA) ranged between 44(7) and 109(22) TBq·mmole-1, while the bindable metals concentration, a metric introduced for assessing and comparing product purity, ranged between 43(7) and 115(27) nmole·g-1.
Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.
Tandem column isolation of zirconium-89 from cyclotron bombarded yttrium targets using an automated fluidic platform: Anion exchange to hydroxamate resin columns
The development of a tandem column purification method for the production of high-purity 89Zr is presented. The primary column was a macroporous strongly basic anion exchange resin on styrene divinylbenzene co-polymer. The secondary column, with an internal volume of ~27 µL, was packed with hydroxamate resin. A condition of inverted selectivity was developed, whereby the 89Zr eluent from the primary column is synonymous with the load condition on the secondary column. The ability to transfer 89Zr from one column to the next allows two sequential column clean-up methods to be performed prior to the final elution of the 89Zr. This approach assures delivery of high purity 89Zr product and assures a 89Zr product that is eluted in a substantially smaller volume than is possible when using the traditionally-employed single hydroxamate resin column method. The tandem column purification process has been implemented into a prototype automated fluidic system. The system is configured with on-line gamma detection so column effluents can be monitored in near-real time. The automated method was tested using seven cyclotron bombarded Y foil targets. It was found that 95.1±1.3% of the 89Zr present in the foils was recovered in the secondary column elution fraction. Furthermore, elution peak analysis of several 89Zr elution profile radiochromatograms made possible the determination of 89Zr recovery as a function of volume; a 89Zr product volume that contains 90% of the mean secondary column elution peak can be obtained in 0.29±0.06 mL (representing 86±5% of the 89Zr activity in the target). This product volume represents a significant increase in isotope product concentration over the predominant method used in the field. In addition to the reduced 89Zr product elution volume, titrations of the 89Zr product with deferoxamine mesylate salt across two preparatory methods resulted in mean effective specific activity (ESA) values of 279 and 340 TBq·mmole-1 and mean bindable metals concentrations ([MB]) of 13.5 and 16.7 nmole·g-1. These ESA and [MB] values infer that the 89Zr product resulting from this tandem column isolation method has the highest purity reported to date.
Manipulation of mass transport rates using bead-in-a-tube method
In ultralow Pu analyses, the gold standard is thermal ionization mass spectrometry (TIMS), which requires pure sources to achieve its performance. This purity is achieved through step-wise purifications. In this work single anion-exchange beads were trapped in tubing to allow for dynamic solution cycling over the surface of the beads to improve the rates of metal complex uptake. Rates of Pu sorption on single ~900 µm SIR-1200 and ~620 µm Reillex-HPQ beads were determined for single beads trapped in a tube with syringe pump driven dynamic solution cycling over the bead, improving sorption and desorption rates. A static control was used as a comparison. Using 238Pu to enable facile activity-based measurements, rates were determined by measuring the residual Pu after contact with beads using liquid scintillation analysis (LSA) for fixed periods of time. Syringe pump driven dynamic solution cycling results in ~5 and ~15-fold improvements in the sorption rates for SIR-1200 and Reillex-HPQ. Impacts on desorption were also examined.
A Methodology and Implementation of Automated Emissions Harmonization for Use in Integrated Assessment Models
Emissions harmonization refers to the process used to match greenhouse gas (GHG) and air pollutant results from Integrated Assessment Models (IAMs) against a common source of historical emissions. To date, harmonization has been performed separately by individual modeling teams. For the hand-over of emission data for the Shared Socio-economic Pathways (SSPs) to climate model groups, a new automated approach based on commonly agreed upon algorithms was developed. This work describes the novel methodology for determining such harmonization methods and an open-source Python software library implementing the methodology. Results are shown for two example scenarios (with and without climate policy cases) using the MESSAGE-GLOBIOM IAM that satisfactorily harmonize over 96% of the total emissions trajectories while having a negligible eect on key long-term climate indicators. This new capability enhances the comparability across dierent models, increases transparency and robustness of results, and allows other teams to easily participate in intercomparison exercises by using the same, openly available harmonization mechanism.
Distribution System Segmented Model Simplification with Independent Dynamically Changing End-Use Loads
With more emerging technologies in power distribution systems, to support quasi-static time-series (QSTS) simulation over short time frames, accurately modeling the dynamic behavior of end-use load under various conditions is im- portant. However, time and cost can be a burden for the QSTS simulation of thousands of houses representing dynamic load behaviors on large distribution feeders. There are two main contributions of this paper in order to reduce the QSTS simulation complexity. First, the paper presents a method to stochasti- cally model the dynamic behavior of end-use load for QSTS simulations. Second, using the stochastically modeled load behavior as an input to a distribution sys- tem, and building on our previous QSTS simulation simplification/acceleration method that relies on uniform load behavior, this paper presents a segment substitution approach for the simplification of distribution system models with independent dynamically changing end-use loads. Our numerical results show that compared to GridLAB-D, a software tool that simulates end-use load be- havior through physical models, the proposed stochastic modeling approach achieved an 89.3% simulation time reduction for end-use loads, while accurately capturing the temporally changing total load behavior. Moreover, the QSTS simulation of a large test distribution system through OpenDSS showed that the proposed simplification method decreased the feeder simulation time by a factor of three at less than 0.3% voltage error. Compared to our previous method with independent dynamic loads, the voltage error is reduced by a factor of four.
Analysis of water–energy nexus and trends in support of the sustainable development goals: A study using longitudinal water–energy use data
Water and energy are two critical natural resources necessary for human activities and socioeconomic development. Water and energy systems are highly interdependent, and water efficiency and energy efficiency are two related indicators for the United Nations' Sustainable Development Goals. It is critical to improve energy–water use efficiency to sustain socioeconomic development while reducing adverse effects on natural resources, society and the environment. By using longitudinal energy–water use data for China over the past 21 years, this paper develops a temporo-spatial study to address key issues and introduce analytical approaches needed to understand the water–energy nexus and support integrated resource planning and management to achieve the Sustainable Development Goals. Decomposition analysis indicates that the production effect was the dominating factor contributing the increase in the country's energy–water use, while energy–water efficiency is the major factor slowing the growth of the country's energy–water use. Changes and trends analyses show that the country's energy intensity, water intensity, and water/energy ratio significantly decreased from 1999 to 2019, but the rate of decline has slowed. The disparities of the country's provincial energy intensities, water intensities, and water/energy ratios significantly decreased with economic growth. Results suggest that improving energy–water efficiency is critical for the country to curb increasing energy and water use and achieve resource and environmental protection targets with rapid economic development. Furthermore, the disparities between regional energy-water efficiencies can be reduced along with economic growth, while an overheated economy can widen the disparities and result in unsustainable and inefficient utilization of resources. Government coordination, targets and policy as part of the efficiency governance system are critical for continuous energy–water efficiency improvement and directly influence the implementation and effectiveness of energy–water efficiency policy.
Enhanced 99 Tc retention in glass waste form using Tc(IV)-incorporated Fe minerals
Technetium (99Tc) immobilization by doping into iron oxide mineral phases may alleviate the problems with Tc volatility during vitrification of nuclear waste. Reduced Tc, Tc(IV), substitutes for Fe(III) in the crystal structure by a process of Tc reduction from Tc(VII) to Tc(IV) followed by co-precipitation of Fe oxide minerals. Two Tc-incorporated Fe minerals (Tc-goethite and Tc-magnetite/maghemite) were prepared and tested for Tc retention in glass melt samples at temperatures between 600 – 1,000 oC. After being cooled, the solid glass specimens prepared at different temperatures were analyzed for Tc oxidation state using Tc K-edge XANES. In most samples, Tc was partially oxidized from Tc(IV) to Tc(VII) as the melt temperature increased. However, Tc retention in glass melt samples prepared using Tc-incorporated Fe minerals were moderately higher than in glass prepared using KTcO4 because of limited and delayed Tc volatilization.
Flow-induced vibration analysis of a helical coil steam generator experiment using large eddy simulation
This paper describes a numerical study of flow-induced vibration in a helical coil steam generator experiment conducted at Argonne National Laboratory in the 1980s. In the experiment, a half-scale sector model of a steam generator helical coil tube bank was subjected to still and flowing air and water, and the vibrational characteristics were recorded. The research detailed in this document utilizes the multi-physics simulation toolkit SHARP developed at Argonne National Laboratory, in cooperation with Lawrence Livermore National Laboratory, to simulate the experiment. SHARP uses the spectral element code Nek5000 for fluid dynamics analysis and the finite element code DIABLO for structural analysis. The flow around the coil tubes is modeled in Nek5000 by using a large eddy simulation turbulence model. Transient pressure data on the tube surfaces is sampled and transferred to DIABLO for the structural simulation. The structural response is simulated in DIABLO via an implicit time-marching algorithm and a combination of continuum elements and structural shells. Tube vibration data (acceleration and frequency) are sampled and compared with the experimental data. Currently, only one-way coupling is used, which means that pressure loads from the fluid simulation are transferred to the structural simulation but the resulting structural displacements are not fed back to the fluid simulation
Use of Physics to Improve Solar Forecast: Part II, Machine Learning and Model Interpretability
Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.
Quantitative Determination of Biomass-derived Renewable Carbon in Fuels from Coprocessing of Bio-oils in Refinery Using a Stable Carbon Isotopic Approach
Increasing renewable carbon incorporation into conventional fuels through coprocessing with vacuum gas oil (VGO, a petroleum refining feedstock) is a critical step in biofuels development, scaling-up, adoption and associated GHG reduction. Optimization of the co-processing parameters maximizes incorporation of the renewable carbon in the fuel products. Quantitative determination of the renewable carbon content in the co-processed products provides direct evaluation of the parameters. The co-processing bio-oil with VGO through hydrocracking (HC) or fluid catalytic cracking (FCC) system resulted in carbon isotopic fractionation that prevented the direct use of the isotope mixing model for quantifying the renewable carbon. Here, we report an algorithm of using a stable carbon isotope approach to quantify the renewable carbon content in co-processing biofuel products through high-precision ?13C analysis. A controlled experiment carried out by blending a fossil diesel (-29.013‰) with a bio-diesel (-30.099‰) at various blending levels up to 98.0/2.0 wt% is presented and has demonstrated the applicability of this approach. The carbon isotope fractionation factors for the bio-oil co-processing were obtained by using a 14C-derived isotope-mixing model. The ?13C method was tested by co-processing 13C-labeled bio-crude and natural woody biomass-derived fast pyrolysis (FP) and catalytic fast pyrolysis (CFP) bio-oils with VGO. The results were verified by 14C accelerator mass spectrometry (AMS) method (ASTM-D6866) and compared with the yield mass balance (YMB) method. Strong agreement between d13C and 14C AMS methods demonstrated the applicability of the ?13C method to quantify renewable carbon content in co-processing fuel products and guide the co-processing optimization