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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Open Source Scalable Data Services and Data Fusion for Biological and Environmental Sciences (SBIR Phase I Final Scientific/ Technical Report)

The overarching goal of the project is to develop an integrated open-source scientific data management system (Apache V2 license), ResonantEco, that meets the need of biological and environmental researchers and developers for data management, curation, and data processing for analyses with a wide range of scale and complexity. ResonantEco will provide web enabled data services with features such as unified data interfaces and federated views of data and metadata for heterogeneous data sources with an interactive web client for data exploration. Our use of the term fusion is taken from geospatial (GIS) domain where data fusion is often synonymous with data integration. In particular, data integration in ResonantEco involves combining data residing in different sources and providing users with a unified view of them.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Tensile and Microindentation Stress-Strain Curves of Al-6061

Recent spherical microindentation stress-strain protocols were developed and validated on Al-6061 (DOI: 10.1186/s40192-016-0054-3). The scaling factor between the uniaxial yield strength and the indentation yield strength was determined to be about 1.9. The microindentation stress-strain protocols were then applied to a microstructurally graded sample in an effort to extract high throughput process-property relationships. The tensile and microindentation force-displacement and stress-strain data are presented in this data set.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Land-Atmosphere Feedback Experiment (LAFE) Science Plan

The Land-Atmosphere Feedback Experiment (LAFE; pronounced “la-fey”) deploys several state-of-the-art scanning lidar and remote sensing systems to the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) site. These instruments will augment the ARM instrument suite in order to collect a data set for studying feedback processes between the land surface and the atmosphere. The novel synergy of remote-sensing systems will be applied for simultaneous measurements of land-surface fluxes and horizontal and vertical transport processes in the atmospheric convective boundary layer (CBL). The impact of spatial inhomogeneities of the soil-vegetation continuum on land-surface-atmosphere (LSA) feedback will be studied using the scanning capability of the instrumentation. The time period of the observations is August 2017, because large differences in surface fluxes between different fields and bare soil can be observed, e.g., pastures versus fields where the wheat has already been harvested. The remote sensing system synergy will consist of three components: 1) The SGP water vapor and temperature Raman lidar (SRL), the SGP Doppler lidar (SDL), and the National Center for Atmospheric Research (NCAR) water vapor differential absorption lidar (DIAL) (NDIAL) mainly in vertical staring modes to measure mean profiles and gradients of moisture, temperature, and horizontal wind. They will also measure profiles of higher-order turbulent moments in the water vapor and wind fields and profiles of the latent heat flux. 2) A novel scanning lidar system synergy consisting of the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Doppler lidar (HRDL), the University of Hohenheim (UHOH) water-vapor differential absorption lidar (UDIAL), and the UHOH temperature Raman lidar (URL). These systems will perform coordinated range-height indicator (RHI) scans from just above the canopy level to the lower troposphere, including the interfacial layer of the CBL. The optimal azimuth is to the ENE of the SGP central facility, which takes advantage of both changes in the surface elevation and different crop types planted along that path. 3) The University of Wisconsin Space Science and Engineering Center Portable Atmospheric Research Center (SPARC) and the University of Oklahoma Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS) operating two vertically pointing atmospheric emitted radiance interferometers (AERIs) and two Doppler lidar (DL) systems scanning cross track to the central RHI for determining the surface friction velocity and the horizontal variability of temperature, moisture, and wind. Thus, both the variability of surface fluxes and CBL dynamics and thermodynamics over the SGP site will be studied for the first time. The combination of these three components will enable us to estimate both the divergence of the latent heat profile and the advection of moisture. Thus, the moisture budget in the SGP domain can be studied. Furthermore, the simultaneous measurements of surface and entrainment fluxes as well as the daily cycle of the CBL thermodynamic state will provide a unique data set for characterizing LSA interaction in dependence of large-scale and local conditions such as soil moisture and the state of the vegetation. The measurements will also be applied for the development of improved parameterizations of surface fluxes and turbulence in the CBL. The latter is possible because mean profiles, gradients, higher-order moments, and fluxes are measured simultaneously. The results will be used for the verification of simulations of LSA feedback in large-eddy simulation (LES) and mesoscale models, which are planned for the SGP site. Due to the strong connection between the pre-convective state of the CBL and the formation of clouds and precipitation, this new generation of experiments will strongly contribute to the improvement of their representation in weather, climate, and earth system models.

Southern Great Plains, atmospheric boundary layer,↗
DOE OSTI2015

Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards

The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

It takes a village: using a crowdsourced approach to investigate organic matter composition in global rivers through the lens of ecological theory

Though community-based scientific approaches are becoming more common, many scientific efforts are conducted by small groups of researchers that together develop a concept, analyze data, and interpret results that ultimately translate into a publication. Here, we present a community effort that breaks these traditional boundaries of the publication process by engaging the scientific community from initial hypothesis generation to final publication. We leverage community-generated data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to study organic matter composition through the lens of ecological theory. This community endeavor will use a suite of paired physical and chemical datasets collected from 97 river corridors across the globe. With our first step aimed at ideation, we engaged a community of scientists from 20 countries and 60 institutions, spanning disciplines and career stages by holding a virtual workshop (April 2021). In the workshop, participants generated content for questions, hypotheses, and proposed analyses based on the WHONDRS dataset. These ideation efforts resulted in several narratives investigating different questions led by different teams, which will be the basis for research articles in a Frontiers in Water collection. Currently, the community is collectively analyzing, interpreting, and synthesizing these data that will result in seven crowdsourced articles using a single, existing WHONDRS dataset. The use of a shared dataset across articles not only lowers barriers for broad participation by not requiring generation of new data, but also provides unique opportunities for emergent learning by connecting outcomes across studies. Here we will explain methods used to enable this community endeavor aimed to promote a greater diversity of thinking on river corridor biogeochemistry through community science.

Borton, Mikayla A.↗
DOE OSTI2017

Reproducible, Component-based Modeling with TopoFlow, A Spatial Hydrologic Modeling Toolkit

Modern geoscientists have online access to an abundance of different data sets and models, but these resources differ from each other in myriad ways and this heterogeneity works against interoperability as well as reproducibility. The purpose of this paper is to illustrate the main issues and some best practices for addressing the challenge of reproducible science in the context of a relatively simple hydrologic modeling study for a small Arctic watershed near Fairbanks, Alaska. This study requires several different types of input data in addition to several, coupled model components. All data sets, model components and processing scripts (e.g. for preparation of data and figures, and for analysis of model output) are fully documented and made available online at persistent URLs. Similarly, all source code for the models and scripts is open-source, version controlled and made available online via GitHub. Each model component has a Basic Model Interface (BMI) to simplify coupling and its own HTML help page that includes a list of all equations and variables used. The set of all model components (TopoFlow) has also been made available as a Python package for easy installation. Three different graphical user interfaces for setting up TopoFlow runs are described, including one that allows model components to run and be coupled as web services.

58 GEOSCIENCES↗
DOE OSTI2015

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows

Here we report that computational science is well established as the third pillar of scientific discovery and is on par with experimentation and theory. However, as we move closer toward the ability to execute exascale calculations and process the ensuing extreme-scale amounts of data produced by both experiments and computations alike, the complexity of managing the compute and data analysis tasks has grown beyond the capabilities of domain scientists. Therefore, workflow management systems are absolutely necessary to ensure current and future scientific discoveries. A key research question for these workflow management systems concerns the performance optimization of complex calculation and data analysis tasks. The central contribution of this article is a description of the PANORAMA approach for modeling and diagnosing the run-time performance of complex scientific workflows. This approach integrates extreme-scale systems testbed experimentation, structured analytical modeling, and parallel systems simulation into a comprehensive workflow framework called Pegasus for understanding and improving the overall performance of complex scientific workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗
DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scaling Containment on a Large Centrifuge

Containment science concerns the trapping or leakage of radioactive cavity gases from underground nuclear explosions (UNEs). The most physically relevant data for validation of containment science comes from legacy UNE testing or large field-scale chemical explosions. Field-scale tests can inform on the degree of residual stress imparted to host geology and possible formation of an accompanying gas-containing “stress cage”, for example, as a function of chemical explosion yield. However, field testing can be expensive, difficult to conduct, and challenging to instrument with limitations on data coverage (i.e., the number of boreholes; difficult coring through damage zones). Small-scale laboratory tests are typically simpler to conduct and more thoroughly characterize, but may not include the right scale of containment processes for stress-cage or chimney formation. This report presents the theory and fundamentals of large centrifuge physical modeling, including a history of explosive or similar testing relevant to containment science investigations on centrifuges. Subscale models with embedded chemical explosives in the enhanced gravity of a large centrifuge can represent hundreds of meters of depth and large explosions not otherwise attainable in the laboratory as based on scaling of length, energy, and other processes by the g-factor of the centrifuge (i.e., the number of times larger the centrifugal force is than the gravitational force on the Earth’s surface). The centrifuge uniquely joins the simplicity and exhaustiveness of laboratory-scale characterization with the physics of field-scale processes. This report is part of Sandia National Laboratories’ (SNL’s) effort to prepare for physical modeling with approximately one-meter tall geologic models of stress cage and/or chimney formation on its 29-foot [8.84 m] radius centrifuge with a load capacity of 1.6 million 𝑔-pounds [726 𝑔-ton]. The scaling relationships herein will inform model design decisions and performance requirements for in-flight sensors.

42 ENGINEERING↗
DOE OSTI2025

Ultimate brightness of a medium-energy synchrotron light source at operational beam intensity

Synchrotron light sources are key instruments of modern science, providing unique opportunities for groundbreaking studies in diverse scientific disciplines and driving innovation in numerous scientific and technological fields. Fourth-generation light sources provide unprecedented capabilities in imaging, spectroscopy and diffraction techniques. Ultimate brightness is the key to advancing to a smaller scale, faster response, and higher data measurement and processing rate. The brightness is primarily determined by the electron beam emittance and energy spread at operational intensity. A common feature of fourth-generation synchrotrons is the short length of the electron bunches combined with a very small transverse beam size. Consequently, the high particle density leads to strong collective effects that significantly increase the emittance and limit the achievable brightness at operational beam intensity. In this article, we summarize our studies of the emittance and brightness scaled with the beam energy and intensity, taking into account the effects of intrabeam scattering, beam-impedance interaction and bunch lengthening provided by higher-harmonic RF systems to identify optimal combinations of machine and beam parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

net radiation↗
DOE OSTI2017

Committee ARM User Executive Committee Meeting Summary March 30-31, 2016

This meeting was held at the National Center for Atmospheric Research (NCAR) Mesa Lab, Boulder, Colorado. Atmospheric Radiation Measurement (ARM) Climate Research Facility User Executive Committee (UEC) members present were: Chuck Long, Ernie Lewis, Larry Berg, Hailong Wang, Matt Shupe, Andrew Gettelman, and Dave Turner. Rob Wood joined the discussion at several points via conference call, as he was unable to attend in person. Gannet Hallar and Pavlos Kollias were unable to attend. ARM Infrastructure members present were: Jim Mather, Jennifer Comstock, Jimmy Voyles, Ken Kehoe, Doug Sisterson, Hanna Goss, and Giri Prakash. The ARM UEC met face to face for the first time since the committee was formed in December, 2014 at the NCAR Mesa Lab in Boulder. Prior to this point, all UEC meetings were done via telephone, usually with the aid of collaboration software (e.g., Lync, GoToMeeting) allowing the entire committee to see a common presentation. These conference calls covered a wide range of topics, many of them on a recurrent basis; however, since the calls were limited to less than 90 minutes it was felt that a more dedicated meeting would allow us to delve more deeply into some of these topics. Three topics for this meeting came from previous conference call discussions: (1) data quality, (2) uncertainty quantification, and (3) improved communications. Two other topics were discussed during this meeting: (4) an overview of the LES (large-eddy simulation) ARM Symbiotic Simulation and Observation (LASSO) project and (5) the process for electing new UEC members. Summaries of each of these topics are provided below, along with recommendations that the UEC feels should be considered by the ARM Facility (which will be highlighted using italics).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. Here, we demonstrate improved processing and laser control performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Planck 2015 results: VII. High Frequency Instrument data processing: Time-ordered information and beams

The Planck High Frequency Instrument (HFI) has observed the full sky at six frequencies (100, 143, 217, 353, 545, and 857 GHz) in intensity and at four frequencies in linear polarization (100, 143, 217, and 353 GHz). In order to obtain sky maps, the time-ordered information (TOI) containing the detector and pointing samples must be processed and the angular response must be assessed. The full mission TOI is included in the Planck 2015 release. This study describes the HFI TOI and beam processing for the 2015 release. HFI calibration and map making are described in a companion paper. The main pipeline has been modified since the last release (2013 nominal mission in intensity only), by including a correction for the nonlinearity of the warm readout and by improving the model of the bolometer time response. The beam processing is an essential tool that derives the angular response used in all the Planck science papers and we report an improvement in the effective beam window function uncertainty of more than a factor of 10 relative to the2013 release. Noise correlations introduced by pipeline filtering function are assessed using dedicated simulations. Finally, angular cross-power spectra using data sets that are decorrelated in time are immune to the main systematic effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

High resolution characterization of soil dissolved organic matter with FTICR-MS (Fourier-transform ion cyclotron resonance mass spectrometry) from soil samples in control and warming plots in Blodgett Forest, CA (2014 and 2018)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.This package contains Fourier transform ion cyclotron resonance mass spectrometry (21 Tesla FTICR-MS) data measured in negative and positive ionization mode from water and methanol soil extracts. Soil samples were collected in 2014/06/03 and 2018/06/04 from 3 replicated paired plots that had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. The following files are included: (1) fticr_neg_h2oMeoh_data_raw.csv: raw data from combined water (H2O) and methanol (MeOH) extracts in negative ion mode, (2) fticr_neg_h2oMeoh_data_processed.csv: processed data from combined water (H2O) and methanol (MeOH) extracts in negative ion mode, (3) fticr_neg_metadata.csv: metadata for samples/measurements in negative ion mode, (4) fticr_pos_h2oMeoh_data_raw.csv: raw data from combined water (H2O) and methanol (MeOH) extracts in positive ion mode, (5) fticr_pos_h2oMeoh_data_processed.csv: processed data from combined water (H2O) and methanol (MeOH) extracts in positive ion mode, (6) fticr_pos_metadata.csv: metadata for samples/measurements in positive ion mode.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of Nuclear Spent Fuel Disposal in Clay-Bearing Rock - Process Model Development and Experimental Studies (M2SF-21SN010301072)

The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress in modeling and experimental approaches towards the characterization of chemical and physical phenomena that could impact the long-term safety assessment of heatgenerating nuclear waste disposition in deep-seated clay/shale/argillaceous rock. International collaboration activities such as heater tests, continuous field data monitoring, and postmortem analysis of samples recovered from these have elucidated key information regarding changes in the engineered barrier system (EBS) material exposed to years of thermal loads. Chemical and structural analyses of sampled bentonite material from such tests as well as experiments conducted on these are key to the characterization of thermal effects affecting bentonite clay barrier performance and the extent of sacrificial zones in the EBS during the thermal period. Thermal, hydrologic, and chemical data collected from heater tests and laboratory experiments has been used in the development, validation, and calibration of THMC simulators to model near-field coupled processes. This information leads to the development of simulation approaches (e.g., continuum and discrete) to tackle issues related to flow and transport at various scales of the host-rock, its interactions with barrier materials, and EBS design concept.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Determination of Material Constitutive Laws for Inconel 718 Superalloy Under Different Strain Rates and Working Temperatures

In this paper, a special procedure for the prediction of parameters of the Johnson–Cook constitutive material models is proposed based on the experimental data and specially developed MATLAB scripts which allow advanced modeling of complex 3D response surfaces. Experimental investigations concern two various strain rates of 10{sup −3} and 10{sup 1} 1/s and the testing temperature ranging from the ambient up to 700 °C. As a result, a set of mathematical equations which fit the experimental data is determined. The applicability of the experimentally derived constitutive models to the FEM modeling of real machining processes of Inconel 718 alloy is verified.

36 MATERIALS SCIENCE↗