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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Atmospheric Research Measurement Climate Research Facility - Atmospheric System Research High-Resolution Modeling Workshop, May 19-20, 2014

The Atmospheric Radiation Measurement (ARM) Climate Research Facility is a scientific user facility within the U.S. Department of Energy (DOE) Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER). ARM is an observation facility whose purpose is to provide ground-based observations of the atmosphere to support climate research and to support the improvement of global climate models. ARM is closely affiliated with the Atmospheric System Research (ASR) program, which uses ARM data to address key atmospheric science issues and improve the parameterization of physical processes in climate models. A common methodology for using ARM data to improve climate models has been to carry out multi-model evaluation projects, typically in conjunction with field campaigns in which intensive radiosonde launches support the development of model forcing data sets. These exercises have yielded important insights. However, in recent years, there has been a growing interest in supporting high-resolution model simulations on a more routine basis. An important aspect of ARM sites is the continuous collection of high-resolution data over long periods of time. More routine model simulations would take better advantage of these long-term data sets. Nevertheless, accomplishing this will require careful attention to the requirements for running and evaluating these models together with a research program that maximizes the benefit of the model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

A study of ion composition and dynamics at Comet Halley

This report details the participation by Lockheed co-investigators in the reduction, analysis, and interpretation of data obtained by the Ion Mass Spectrometer onboard the Giotto mission to Comet Halley. The data analysis activities and much of the scientific collaboration was shared by this team. One objective of the effort under this contract was to use data obtained by the Giotto Ion Mass Spectrometer (IMS) during the encounter with comet Halley for the purpose of advancing our understanding of the chemistry and physics of the interaction of the solar wind with comets and obtaining new information on the comet's composition. An additional objective was to make this unique data set available in a format which can be easily used by the reset of the cometary science community for other analysis in the future. The IMS has two sensors: the High Intensity Spectrometer (HIS) and the High Energy Range Spectrometer (HERS).

Shelley, E. G.↗
DOE OSTI2011

Power- and Cooling-Aware Parallel Performance Diagnosis

Increasing concern about the power consumption of data centers and computer laboratories, which in some cases matches or exceeds the resources required to power a small city, drive a need for a new, integrated approach to parallel performance diagnosis that integrates traditional application oriented performance data with measurements of the physical runtime environment. We have developed infrastructure for combined evaluation of system, application, and machine room performance in the high end environment. We motivate our approach, with a case study of the performance, power and cooling impact of the choice of physical location for a scientific application within the machine room. We present a new intensity metric for use in automated performance diagnosis tools, and discuss the challenges encountered.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences

Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1... λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.

Eitel, Jan U. H.↗
DOE OSTI2016

Morphology and Evolution of 14Cr Powder During Mechanical Alloying for Oxide Dispersion Strengthened Ferritic Alloys

Oxide Dispersion Strengthened (ODS) Ferritic steel alloys are candidate materials for Generation IV and Fusion reactor concepts due to their high temperature material properties and their irradiation stability. However, these material properties are strongly influenced by nanocluster dispersion and grain size distributions, which are directly correlated to mechanical alloying methodologies and parameters. Current powder processing strategies use ball milling times greater than or equal to 40 hrs to achieve sufficient dispersoid, i.e Y{sub 2}O{sub 3}, incorporation within the metal matrix, but these time intensive milling cycles are neither practical nor economical. This work aims to investigate how the particle morphology evolves during mechanical alloying during the processing of a 14Cr ODS ferritic alloy, and if higher-intensity milling, shorter milling cycles can still achieve sufficient mixing of yttria in comparison to the dispersion and incorporation observed in conventional lower-intensity milling methodologies. Comparisons of milled particle size distributions are presented for low and high intensity milling cycles, while microscopy and high-temperature tensile data are presented for consolidated samples using higher-intensity milling parameters. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2019

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis

Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments.

Sullivan, Brendan T.↗
DOE OSTI2022

AMX – the highly automated macromolecular crystallography (17-ID-1) beamline at the NSLS-II

The highly automated macromolecular crystallography beamline AMX/17-ID-1 is an undulator-based high-intensity (>5 × 10 12 photons s −1 ), micro-focus (7 µm × 5 µm), low-divergence (1 mrad × 0.35 mrad) energy-tunable (5–18 keV) beamline at the NSLS-II, Brookhaven National Laboratory, Upton, NY, USA. It is one of the three life science beamlines constructed by the NIH under the ABBIX project and it shares sector 17-ID with the FMX beamline, the frontier micro-focus macromolecular crystallography beamline. AMX saw first light in March 2016 and started general user operation in February 2017. At AMX, emphasis has been placed on high throughput, high capacity, and automation to enable data collection from the most challenging projects using an intense micro-focus beam. Here, the current state and capabilities of the beamline are reported, and the different macromolecular crystallography experiments that are routinely performed at AMX/17-ID-1 as well as some plans for the near future are presented.

36 MATERIALS SCIENCE↗
NASA NTRS1999

EOS Terra Validation Program

The EOS Terra mission will be launched in July 1999. This mission has great relevance to the atmospheric radiation community and global change issues. Terra instruments include ASTER, CERES, MISR, MODIS and MOPITT. In addition to the fundamental radiance data sets, numerous global science data products will be generated, including various Earth radiation budget, cloud and aerosol parameters, as well as land surface, terrestrial ecology, ocean color, and atmospheric chemistry parameters. Significant investments have been made in on-board calibration to ensure the quality of the radiance observations. A key component of the Terra mission is the validation of the science data products. This is essential for a mission focused on global change issues and the underlying processes. The Terra algorithms have been subject to extensive pre-launch testing with field data whenever possible. Intensive efforts will be made to validate the Terra data products after launch. These include validation of instrument calibration (vicarious calibration) experiments, instrument and cross-platform comparisons, routine collection of high quality correlative data from ground-based networks, such as AERONET, and intensive sites, such as the SGP ARM site, as well as a variety field experiments, cruises, etc. Airborne simulator instruments have been developed for the field experiment and underflight activities including the MODIS Airborne Simulator (MAS), AirMISR, MASTER (MODIS-ASTER), and MOPITT-A. All are integrated on the NASA ER-2, though low altitude platforms are more typically used for MASTER. MATR is an additional sensor used for MOPITT algorithm development and validation. The intensive validation activities planned for the first year of the Terra mission will be described with emphasis on derived geophysical parameters of most relevance to the atmospheric radiation community. Detailed information about the EOS Terra validation Program can be found on the EOS Validation program homepage i/e.: http://ospso.gsfc.nasa.gov/validation/valpage.html).

Starr, David↗
NASA NTRS2000

EOS Terra Validation Program

The EOS Terra mission will be launched in July 1999. This mission has great relevance to the atmospheric radiation community and global change issues. Terra instruments include Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Clouds and Earth's Radiant Energy System (CERES), Multi-Angle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements of Pollution in the Troposphere (MOPITT). In addition to the fundamental radiance data sets, numerous global science data products will be generated, including various Earth radiation budget, cloud and aerosol parameters, as well as land surface, terrestrial ecology, ocean color, and atmospheric chemistry parameters. Significant investments have been made in on-board calibration to ensure the quality of the radiance observations. A key component of the Terra mission is the validation of the science data products. This is essential for a mission focused on global change issues and the underlying processes. The Terra algorithms have been subject to extensive pre-launch testing with field data whenever possible. Intensive efforts will be made to validate the Terra data products after launch. These include validation of instrument calibration (vicarious calibration) experiments, instrument and cross-platform comparisons, routine collection of high quality correlative data from ground-based networks, such as AERONET, and intensive sites, such as the SGP ARM site, as well as a variety field experiments, cruises, etc. Airborne simulator instruments have been developed for the field experiment and underflight activities including the MODIS Airborne Simulator (MAS) AirMISR, MASTER (MODIS-ASTER), and MOPITT-A. All are integrated on the NASA ER-2 though low altitude platforms are more typically used for MASTER. MATR is an additional sensor used for MOPITT algorithm development and validation. The intensive validation activities planned for the first year of the Terra mission will be described with emphasis on derived geophysical parameters of most relevance to the atmospheric radiation community.

Starr, David↗
DOE OSTI2022

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A new ATLAS muon CSC readout system with system on chip technology on ATCA platform

The ATLAS muon Cathode Strip Chamber (CSC) back-end readout system has been upgraded during the LHC 2013-2015 shutdown to be able to handle the higher Level-1 trigger rate of 100 kHz and the higher occupancy at Run 2 luminosity. The readout design is based on the Reconfiguration Cluster Element (RCE) concept for high bandwidth generic DAQ implemented on the ATCA platform. The RCE design is based on the new System on Chip Xilinx Zynq series with a processor-centric architecture with ARM processor embedded in FPGA fabric and high speed I/O resources together with auxiliary memories to form a versatile DAQ building block that can host applications tapping into both software and firmware resources. The Cluster on Board (COB) ATCA carrier hosts RCE mezzanines and an embedded Fulcrum network switch to form an online DAQ processing cluster. More compact firmware solutions on the Zynq for G-link, S-link and TTC allowed the full system of 320 G-links from the 32 chambers to be processed by 6 COBs in one ATCA shelf through software waveform feature extraction to output 32 S-links. Furthermore, the full system was installed in Sept. 2014. We will present the RCE/COB design concept, the firmware and software processing architecture, and the experience from the intense commissioning towards LHC Run 2.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Accelerating Discovery of Atomistic Defects via Machine Learning

The quantification of defects such as vacancies in crystalline structures is a cornerstone of materials science research. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within a crystalline lattice, aiming to expedite detection while improving accuracy. Additionally, we explore the transferability of these ML techniques, identifying characteristics of atomistic imaging data that complicate this task. We show how the integration of ML can drive innovation, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2016

Report of the 2014 Programming Models and Environments Summit

Programming models and environments play the essential roles in high performance computing of enabling the conception, design, implementation and execution of science and engineering application codes. Programmer productivity is strongly influenced by the effectiveness of our programming models and environments, as is software sustainability since our codes have lifespans measured in decades, so the advent of new computing architectures, increased concurrency, concerns for resilience, and the increasing demands for high-fidelity, multi-physics, multi-scale and data-intensive computations mean that we have new challenges to address as part of our fundamental R&D requirements. Fortunately, we also have new tools and environments that make design, prototyping and delivery of new programming models easier than ever. The combination of new and challenging requirements and new, powerful toolsets enables significant synergies for the next generation of programming models and environments R&D. This report presents the topics discussed and results from the 2014 DOE Office of Science Advanced Scientific Computing Research (ASCR) Programming Models & Environments Summit, and subsequent discussions among the summit participants and contributors to topics in this report.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Bhutan Agriculture II: Creating a Graphical User Interface, Crop Mask, and Data Collection Protocol for Analysis of Rice Crop in Bhutan Using Remotely Sensed Data

Agriculture is an important sector in Bhutan, accounting for 19.63% of Bhutan’s GDP in 2020 (World Bank) while also providing livelihoods for approximately 57% of the population (World Bank, 2017). The Department of Agriculture (DoA) in Bhutan still relies on in-field reporting for crop monitoring, which is time-consuming and labour intensive. To promote efficiency in these efforts, the team partnered with the DoA, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER). The team, with the help of the science advisors from NASA SERVIR, expanded the crop mask created in the previous term to the whole country of Bhutan and streamlined the sampling protocols for applicability to any available crop data. The team also created a graphical user interface (GUI) which provided a visual representation of current trends and rice distribution across Bhutan. The team utilized NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM), as well as other Earth observations including Sentinel-1 C-band Synthetic Aperture Radar (C-SAR). This project refined the previous term’s methodology to help supplement crop monitoring and increase the frequency of data collected to aid decision-making processes with the use of remote sensing data.

Wangdrak Dorji​↗
DOE OSTI2022

COMAP Early Science. V. Constraints and Forecasts at z ~ 3

We present the current state of models for the z ~ 3 carbon monoxide (CO) line intensity signal targeted by the CO Mapping Array Project (COMAP) Pathfinder in the context of its early science results. Our fiducial model, relating dark matter halo properties to CO luminosities, informs parameter priors with empirical models of the galaxy–halo connection and previous CO (1–0) observations. The Pathfinder early science data spanning wavenumbers k = 0.051–0.62 Mpc –1 represent the first direct 3D constraint on the clustering component of the CO (1–0) power spectrum. Our 95% upper limit on the redshift-space clustering amplitude A clust ≲ 70 μK 2 greatly improves on the indirect upper limit of 420 μK 2 reported from the CO Power Spectrum Survey (COPSS) measurement at k ~ 1 Mpc –1 . The COMAP limit excludes a subset of models from previous literature and constrains interpretation of the COPSS results, demonstrating the complementary nature of COMAP and interferometric CO surveys. Using line bias expectations from our priors, we also constrain the squared mean line intensity–bias product, ${\left\langle {Tb}\right\rangle }_{2}$ ≲ 50 μK 2 , and the cosmic molecular gas density, ρ H2 < 2.5 × 10 8 M ⊙ Mpc –3 (95% upper limits). Based on early instrument performance and our current CO signal estimates, we forecast that the 5 yr Pathfinder campaign will detect the CO power spectrum with overall signal-to-noise ratio of 9–17. Between then and now, we also expect to detect the CO–galaxy cross-spectrum using overlapping galaxy survey data, enabling enhanced inferences of cosmic star formation and galaxy evolution history.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Blending Education and Polymer Science: Semiautomated Creation of a Thermodynamic Property Database

Structured databases of chemical and physical properties play a central role in the everyday research activities of scientists and engineers. In materials science, researchers and engineers turn to these databases to quickly query, compare, and aggregate various properties, thereby allowing for the development or application of new materials. The vast majority of these databases have been generated manually, through decades of labor-intensive harvesting of information from the literature, yet while there are many examples of commonly used databases, a significant number of important properties remain locked within the tables, figures, and text of publications. The question addressed in our work is whether and to what extent the process of data collection can be automated. Students of the physical sciences and engineering are often confronted with the challenge of finding and applying property data from the literature, and a central aspect of their education is to develop the critical skills needed to identify such data and discern their meaning or validity. To address shortcomings associated with automated information extraction while simultaneously preparing the next generation of scientists for their future endeavors, we developed a novel course-based approach in which students develop skills in polymer chemistry and physics and apply their knowledge by assisting with the semiautomated creation of a thermodynamic property database.

Collaborative/Cooperative Learning↗