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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ICED: An Integrated CGRA Framework Enabling DFVS-Aware Acceleration

oarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. Existing CGRA mapping approaches extract instruction-level parallelism, exploit loop-pipelining opportunities, guarantee the data dependency, and target high throughput of a given loop. However, the recurrence data-dependency in the DFG and the mismatch between required and available computing/communication resources complicate the mapping, and might lead to significant unbalances in the utilization of the CGRA's tiles. This results in wasted power for tiles with low utilization. Applying dynamic voltage and frequency scaling (DVFS) can potentially solve this challenge and improve energy efficiency by adjusting voltage and frequency of different tiles independently. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non performance-constraining kernels. This paper proposes ICEDTEA -- an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICEDTEA proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICEDTEA is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICEDTEA improves average utilization by 2.3$\times$ and energy-efficiency by 1.32$\times$ over a conventional CGRA. With streaming applications, ICEDTEA improves energy efficiency by 1.12$\times$ over a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.

Tan, Cheng↗
DOE OSTI2020

Beam-ion Studies in NSTX and NSTX-U (Final Technical Report)

The confinement of high energy "fast ions" is crucial for the success of magnetic fusion as a practical energy source. The research performed by UC Irvine on NSTX and NSTX-U provided new information about this important topic in the configuration known as a "spherical tokamak." Injected neutral beams provided the fast ions (also known as "beam ions"). There were five overarching goals of the research. One objective was to measure the confinement of beam ions in order to ascertain if they were accomplishing their desired purpose of transferring their energy to the bulk plasma. A second goal was to better understand instabilities that are driven unstable by the fast ions. A related goal was to use the similarities and differences between NSTX and the DIII-D conventional tokamak to better understand the fast-ion driven instabilities. A fourth goal was to develop new instruments to measure fast ions and techniques that facilitate analysis of the data. The fifth objective was to measure and understand acceleration of beam ions by RF waves. Much was accomplished in all of these five areas. In the first, it was shown that the large magnetic moment of the beam ions did not harm confinement but instabilities known as the "sawtooth" and "long-lived mode" do. In the second area, much attention was devoted to the fast-ion instabilities known as "Alfvén eigenmodes." In particular, Alfvén eigenmode "avalanches" can cause ~ 30% of the fast ions to be lost in a single explosive burst. The comparative studies with DIII-D showed that an instability discovered on NSTX is also important in conventional tokamaks. In the fourth category, several instruments were developed, a number of effects that complicate interpretation of the data were understood, and progress toward a new method to infer the fast-ion distribution function from the data was made. In the fifth category, the measured profile of accelerated fast ions was initially much broader than theoretical predictions but subsequent improvements in the theoretical modeling achieved good agreement with the data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Cosmological constraints from higher redshift gamma-ray burst, H ii starburst galaxy, and quasar (and other) data

ABSTRACT We use higher redshift gamma-ray burst (GRB), H ii starburst galaxy (H iiG), and quasar angular size (QSO-AS) measurements to constrain six spatially flat and non-flat cosmological models. These three sets of cosmological constraints are mutually consistent. Cosmological constraints from a joint analysis of these data sets are largely consistent with currently accelerating cosmological expansion and with cosmological constraints derived from a combined analysis of Hubble parameter (H(z)) and baryon acoustic oscillation (BAO, with Planck-determined baryonic matter density) measurements. A joint analysis of the H(z) + BAO + QSO-AS + H iiG + GRB data provides fairly model-independent determinations of the non-relativistic matter density parameter $\Omega _{\rm m_0}=0.313\pm 0.013$ and the Hubble constant $H_0=69.3\pm 1.2\, \rm {km \, s^{-1} \, Mpc^{-1}}$. These data are consistent with the dark energy being a cosmological constant and with spatial hypersurfaces being flat, but they do not rule out mild dark energy dynamics or a little spatial curvature. We also investigate the effect of including quasar flux measurements in the mix and find no novel conclusions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
NASA NTRS2018

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate “open science” biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from “omics” (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

genome↗
NASA NTRS2018

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate "open science" biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from "omics" (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

genome↗
NASA NTRS2017

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate "open science" biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from "omics" (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

Berrios, Daniel C.↗
DOE OSTI2023

Equation-based and data-driven modeling: Open-source software current state and future directions

Here, a review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Use of active control technology to improve ride qualities of large transport aircraft

Analyses, construction and flight testing of two systems: Beta-vane and Modal Suppression Augmentation System (MSAS), which were developed to suppress gust induced lateral accelerations of large aircraft, are described. The 747 transport was used as the test vehicle. The purpose of the Beta-vane system is to reduce acceleration levels at the dutch roll frequency whereas the function of the MSAS system is to reduce accelerations due to flexible body motions caused by turbulence. Data from flight test, with both systems engaged shows a 50 to 70 percent reduction in lateral aft body acceleration levels. Furthermore, it is suggested that present day techniques used for developing dynamic equations of motion in the flexible mode region are limited.

Cohen, G. C.↗
NASA NTRS1993

A comparison of low-gravity measurements on-board Columbia during STS-40

The first NASA Spacelab Life Sciences mission (SLS-1) flew 5 Jun. to 14 Jun. 1991 on the orbiter Columbia (STS-40). The purpose of the mission was to investigate the human body's adaptation to the low-gravity conditions of space flight and the body's readjustment after the mission to the 1 g environment of earth. In addition to the life sciences experiments manifested for the Spacelab module, a variety of experiments in other scientific disciplines flew in the Spacelab and in Get Away Special (GAS) Canisters on the GAS Bridge Assembly. Several principal investigators designed and flew specialized accelerometer systems to better assess the results of their experiments by means of a low-gravity environment characterization. This was also the first flight of the NASA Microgravity Science and Applications Division (MSAD) sponsored Space Acceleration Measurement System (SAMS) and the first flight of the NASA Orbiter Experiments Office (OEX) sponsored Orbital Acceleration Research Experiment accelerometer (OARE). A brief introduction to seven STS-40 accelerometer systems are presented and the resulting data are discussed and compared. During crew sleep periods, acceleration magnitudes in the 10(exp -6) to 10(exp -5) g range were recorded in the Spacelab module and on the GAS Bridge Assembly. Magnitudes increased to the 10(exp -4) g level during periods of nominal crew activity. Vernier thruster firings caused acceleration shifts on the order of 10(exp -4) g and primary thruster firings caused accelerations as great as 10(exp -2) g. Frequency domain analysis revealed typical excitation of Orbiter and Spacelab structural modes at 3.5, 4.7, 5.2, 6.2, 7, and 17 Hz.

Rogers, Melissa J. B.↗
DOE OSTI2025

Recommendations for Best Practices for Data Preservation and Open Science in HEP

These recommendations are the result of reflections by scientists and experts who are, or have been, involved in the preservation of high-energy physics data. The work has been done under the umbrella of the Data Lifecycle panel of the International Committee of Future Accelerators (ICFA), drawing on the expertise of a wide range of stakeholders. A key indicator of success in the data preservation efforts is the long-term usability of the data. Experience shows that achieving this requires providing a rich set of information in various forms, which can only be effectively collected and preserved during the period of active data use. The recommendations are intended to be actionable by the indicated actors and specific to the particle physics domain. They cover a wide range of actions, many of which are interdependent. These dependencies are indicated within the recommendations and can be used as a road map to guide implementation efforts. These recommendations are best accessed and viewed through the web application, see https://icfa-data-best-practices.app.cern.ch/

Campana, Simone [CERN]↗
DOE OSTI2021

Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments

We present a deep learning based framework for real-time analysis of a differential filter based x-ray spectrometer that is common on short-pulse laser experiments. The analysis framework was trained with a large repository of synthetic data to retrieve key experimental metrics, such as slope temperature. With traditional analysis methods, these quantities would have to be extracted from data using a time-intensive and manual analysis. Furthermore, this framework was developed for a specific diagnostic, but may be applicable to a wide variety of diagnostics common to laser experiments and thus will be especially crucial to the development of high-repetition rate (HRR) diagnostics for HRR laser systems that are coming online.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

PV Degradation Modeling: Applying Geospatial Workflows with "PVDeg"

Accurate degradation modeling is essential for predicting photovoltaic (PV) module performance, estimating longevity and informing design decisions. With degradation rates varying significantly by location, geospatial analysis is critical for PV and broader applications, such as agrivoltaics, weathering and environmental data analysis. This work presents PVDeg, an open-source tool designed for geospatial degradation analysis. PVDeg integrates meteorological data from global sources, including the National Solar Radiation Database (NSRDB) and Photovoltaic Geographical Information System (PVGIS), with degradation models. The toolkit enables users to customize geospatial workflows by integrating weather data, material parameters, and user-defined Python functions. It facilitates accelerated downloads of NSRDB and PVGIS datasets and optimizes geospatial point selection to preserve data density in regions of interest. Additionally, PVDeg provides a local database for storage and spatial queries, supporting large-scale analyses without the need for high-performance computing (HPC) resources. PVDeg provides a foundational workflow that extends its utility beyond PV applications, enabling researchers to analyze geospatial processes across discipline.

14 SOLAR ENERGY↗
NASA NTRS1976

Explosive and pyrotechnic aging demonstration

The survivability was experimentally verified of fine selected explosive and pyrotechnic propellant materials when subjected to sterilization, and prolonged exposure to space environments. This verification included thermal characterization, sterilization heat cycling, sublimation measurements, isothermal decomposition measurements, and accelerated aging at a preselected elevated temperature. Temperatures chosen for sublimation and isothermal decomposition measurements were those in which the decomposition processess occurring would be the same as those taking place in real-time aging. The elevated temperature selected (84 C) for accelerated aging was based upon the parameters calculated from the kinetic data obtained in the isothermal measurement tests and was such that one month of accelerated aging in the laboratory approximated one year of real-time aging at 66 C. Results indicate that HNS-IIA, pure PbN6, KDNBF, and Zr/KC10 are capable of withstanding sterilization. The accelerated aging tests indicated that unsterilized HNS-IIA and Zr/KC104 can withstand the 10 year, elevated temperature exposure, pure PbN6 and KDNBF exhibit small weight losses (less than 2 percent) and B/KC104 exhibits significant changes in its thermal characteristics. Accelerated aging tests after sterilization indicated that only HNS-IIA exhibited high stability.

Rouch, L. L., Jr.↗
DOE OSTI2022

What We Learned From Analyzing 18 Million Rows of Commercial Buildings’ HVAC Fault Data

To achieve ambitious decarbonization goals it is critical that buildings operate to their full potential. Commercial HVAC systems, however, experience a wide range of operational faults, adversely affecting energy consumption, occupant comfort, and maintenance costs. Analytical tools such as fault detection & diagnostics (FDD) software identify and help diagnose these types of sensing, mechanical, or control-related faults. While significant energy savings has been documented for FDD, along with limited-scale studies on technical capabilities, there is a lack of empirical data on faults being reported by FDD tools. With FDD deployment accelerating significantly over the past decade there is an opportunity to gather and analyze data on commercial HVAC operational problems at an unprecedented scale. Such data could address many questions such as: [a] What faults are most commonly reported?; and [b] How does fault reporting vary by time of year and other possible drivers? A recent study into FDD fault reporting amassed the largest U.S. dataset of commercial HVAC air-side fault records, drawn from multi-year monitoring across over 60,000 pieces of HVAC equipment. The results of this study provide granular data on fault reporting for over 90 unique fault types. In this paper we provide an overview of the research process and highlight key findings and lessons learned. This study presents an extraordinary level of detail on FDD fault reporting characteristics across many climate zones and building types. Armed with these new insights, commercial building industry stakeholders can make better informed decisions when designing, configuring, and operating commercial HVAC systems.

Crowe, Eliot↗
NASA NTRS2002

Translation Optics for 30 cm Ion Engine Thrust Vector Control

Data were obtained from a 30 cm xenon ion thruster in which the accelerator grid was translated in the radial plane. The thruster was operated at three different throttle power levels, and the accelerator grid was incrementally translated in the X, Y, and azimuthal directions. Plume data was obtained downstream from the thruster using a Faraday probe mounted to a positioning system. Successive probe sweeps revealed variations in the plume direction. Thruster perveance, electron backstreaming limit, accelerator current, and plume deflection angle were taken at each power level, and for each accelerator grid position. Results showed that the thruster plume could easily be deflected up to six degrees without a prohibitive increase in accelerator impingement current. Results were similar in both X and Y direction.

Haag, Thomas↗