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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Impacts of Spatial Heterogeneity and Temporal Non-Stationarity on Intensity-Duration-Frequency Estimates—A Case Study in a Mountainous California-Nevada Watershed

Changes in extreme precipitation events may require revisions of civil engineering standards to prevent water infrastructures from performing below the designated guidelines. Climate change may invalidate the intensity-duration-frequency (IDF) computation that is based on the assumption of data stationarity. Efforts in evaluating non-stationarity in the annual maxima series are inadequate, mostly due to the lack of long data records and convenient methods for detecting trends in the higher moments. In this study, using downscaled high resolution climate simulations of the historical and future periods under different carbon emission scenarios, we tested two solutions to obtain reliable IDFs under non-stationarity: (1) identify quasi-stationary time windows from the time series of interest to compute the IDF curves using data for the corresponding time windows; (2) introduce a parameter representing the trend in the means of the extreme value distributions. Focusing on a mountainous site, the Walker Watershed, the spatial heterogeneity and variability of IDFs or extremes are evaluated, particularly in terms of the terrain and elevation impacts. We compared observations-based IDFs that use the stationarity assumption with the two approaches that consider non-stationarity. The IDFs directly estimated based on the traditional stationarity assumption may underestimate the 100-year 24-h events by 10% to 60% towards the end of the century at most grids, resulting in significant under-designing of the engineering infrastructure at the study site. Strong spatial heterogeneity and variability in the IDF estimates suggest a preference for using high resolution simulation data for the reliable estimation of exceedance probability over data from sparsely distributed weather stations. Discrepancies among the three IDFs analyses due to non-stationarity are comparable to the spatial variability of the IDFs, underscoring a need to use an ensemble of non-stationary approaches to achieve unbiased and comprehensive IDF estimates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Facility for testing solar cells

Primary components of facility are test chamber and external solar simulator. Voltage--current performance characteristics of solar cells at various combinations of temperature and light intensity are plotted on X-Y recorder. Data are fed into computer for calculation of maximum power, curve shape factor, cell efficiency, and averages of each parameter.

Yasui, R. K.↗
NASA NTRS1985

A computer controlled mass spectrometer system for investigating the decomposition of non-metallic materials under atmospheric conditions

A PDP 11/23 quadrupole mass spectrometer system was coupled to a nondiscriminating gas inlet system permitting gases at atmospheric pressure to be admitted into a high vacuum chamber containing the ion source of the mass spectrometer without separation of the gaseous components. The resolution of related software problems has resulted in a convenient computer-mass spectrometer system capable of generating masses, relative intensities and related data on the gaseous products resulting from the atmospheric thermal decomposition of nonmetallic materials.

Thompson, J. M.↗
NASA NTRS1999

Effects of square-wave and simulated natural light-dark cycles on hamster circadian rhythms

Circadian rhythms of activity (Act) and body temperature (Tb) were recorded from male Syrian hamsters under square-wave (LDSq) and simulated natural (LDSN, with dawn and dusk transitions) light-dark cycles. Light intensity and data sampling were under the synchronized control of a laboratory computer. Changes in reactive and predictive onsets and offsets for the circadian rhythms of Act and Tb were examined in both lighting conditions. The reactive Act onset occurred 1.1 h earlier (P < 0.01) in LDSN than in LDSq and had a longer alpha-period (1.7 h; P < 0.05). The reactive Tb onset was 0.7 h earlier (P < 0.01) in LDSN. In LDSN, the predictive Act onset advanced by 0.3 h (P < 0.05), whereas the Tb predictive onset remained the same as in LDSq. The phase angle difference between Act and Tb predictive onsets decreased by 0.9 h (P < 0.05) in LDSN, but the offsets of both measures remained unchanged. In this study, animals exhibited different circadian entrainment characteristics under LDSq and LDSN, suggesting that gradual and abrupt transitions between light and dark may provide different temporal cues.

NASA Discipline Regulatory Physiology↗
NASA NTRS2018

Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) Version 4.0: User Manual

The IHEAT v4.0 software is a data reduction code for global thermography data acquired in the NASA Langley Aerothermodynamics Laboratory (LAL) hypersonic wind tunnels. IHEAT uses red and green color-intensity data from two-dimensional images of wind tunnel models to compute temperatures and heat-transfer rates using a semi-infinite, one-dimensional heat transfer approximation at each image pixel. Multiple automated tools in IHEAT v4.0 decrease the time required to reduce the data from a phosphor thermography wind tunnel run. Data at one or all of the image pixel locations can be exported to computer files for further analysis. The prior version of IHEAT, v3.2, was written in PV-WAVE® (now owned by Rogue Wave® Software) in 1994 and was limited in functionality to fit within the memory constraints of the available computers at the time. IHEAT v4.0 is written in MATLAB® by MathWorks® and contains several new features that leverage the increase in available memory of the current computers. A Piecewise tool permits the user to extract data along a segmented line cut that can follow interesting features in the image better than the single, straight line cuts that were possible with the legacy Length and Profile tools. The new Load Run and Batch tools facilitate batch processing by loading in all of the input files and images for a run at the same time. Load Run permits the user to process the available run images manually, while Batch automatically saves heat transfer data from all of the images based on the analysis previously performed on a single frame. IHEAT v4.0 also can automatically calculate the temporal collapse of reference line cuts from the time history heating data for a run to indicate the appropriate frame to reduce for each run. The IHEAT v4.0 source code was compiled into a standalone executable file that can be accessed remotely from several computers with different operating systems, simultaneously. The software is run through the MATLAB® Compiler Runtime engine, and therefore, IHEAT does not require a software license to run. Any software commands executed in the IHEAT v4.0 code will not affect other similar applications running on the same machine. Similarly, changes to the parent software do not affect a compiled code. These features of IHEAT v4.0 are improvements over the legacy v3.2 code, which required regular maintenance to avoid losing functionality as the PVWAVE ® programming language was upgraded.

Mason, Michelle L.↗
DOE OSTI2019

BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS

Modern experiments collect peta-scale volumes of data and utilize vast, geographically distributed computing infrastructure that serves thousands of scientists around the world. Requirements for rapid, near real-time data processing, fast analysis cycles and need to run massive detector simulations to support data analysis pose special premium on efficient use of available computational resources. A sophisticated Workload Management System (WMS) is needed to coordinate the distribution and processing of data and jobs in such environment. The ATLAS experiment at CERN uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. While PanDAcurrently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, it runs around 2 million jobs per day on hundreds of Grid sites and serving thousands of ATLAS users. In 2017 about 1.5 exabytes of data were processed with PanDA.In 2012 BigPanDA project project was started with aim to introduce new types of computing resources into ATLAS computing infrastructure, but also to offering PanDA features to different data-intensive applications for projects and experiments outside of ATLAS and High-Energy and Nuclear Physics. In this article we will present accomplishments and discuss possible directions for future work.

Svirin, Pavlo↗
DOE OSTI2017

Skills and Knowledge for Data-Intensive Environmental Research

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

CMS resource utilization and limitations on the grid after the first two years of LHC collisions

After years of development, the CMS distributed computing system is now in full operation. The LHC continues to set records for operational performance, and CMS records data at more than 300 Hz. Because of the intensity of the beams, there are multiple proton-proton interactions per beam crossing, leading to ever-larger event sizes and processing times. The CMS computing system has responded admirably to these challenges, but some reoptimization of the computing model has been required to maximize the efficient delivery of data analysis results by the collaboration in the face of increasingly constrained computing resources. We present the current status of the system, describe the recent performance, and discuss the challenges ahead and how CMS intends to meet them.

Bagliesi, Giuseppe↗
DOE OSTI2022

Extending and Smoothing Two-dimension Equation of State Simulation Data

The Multiphase Equation of State (MEOS) project works to produce high quality equation of state tables which are used in computationally intensive simulations of materials in different conditions. An equation of state table describes the state of matter under certain physical conditions such as pressure, volume, temperature, or internal energy. MEOS uses many different models and combinations thereof to produce accurate tables consisting of continuous smooth data, derivatives, and higher-order derivatives. Accurate and smooth data are important factors in producing precise simulations. This report focuses on improving the electron tables produced from Purgatorio and Thomas-Fermi data. Purgatorio data is extremely accurate but often jagged and discontinuous in the lower temperature and density region. Thomas-Fermi is a model that provides smooth data throughout. Together, this produces an EOS data table that is both accurate and smooth. This report describes the implementation of a new feature that allows users to define more detailed regions in the Purgatorio table to be replaced with Thomas-Fermi. As a result, MEOS can generate a table that is both more smooth and still accurate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has not yet been shown that a single form of learning or training is capable of addressing a large range of scenarios. As a result, solving the first challenge does not inherently solve the second and vice versa. The work described here discusses an approach to address the first challenge that would also provide a foundation for solving the second. Our approach utilizes a combination of conditional imitation learning with a static dataset, reinforcement learning with a simulation environment, and high-performance computing to train a neural network. As a result, this reduces the “time to solution” from to the existing techniques for autonomous driving and provides an extensible framework to address the second key challenge.

autonomous vehicles↗
NASA NTRS2002

Numerical Simulation of Screech Tones from Supersonic Jets: Physics and Prediction

The objectives of this project are to: (1) perform a numerical simulation of the jet screech phenomenon; and (2) use the data of the simulations to obtain a better understanding of the physics of jet screech. The original grant period was for three years. This was extended at no cost for an extra year to allow the principal investigator time to publish the results. We would like to report that our research work and results (supported by this grant) have fulfilled both objectives of the grant. The following is a summary of the important accomplishments: (1) We have now demonstrated that it is possible to perform accurate numerical simulations of the jet screech phenomenon. Both the axisymmetric case and the fully three-dimensional case were carried out successfully. It is worthwhile to note that this is the first time the screech tone phenomenon has been successfully simulated numerically; (2) All four screech modes were reproduced in the simulation. The computed screech frequencies and intensities were in good agreement with the NASA Langley Research Center data; (3) The staging phenomenon was reproduced in the simulation; (4) The effects of nozzle lip thickness and jet temperature were studied. Simulated tone frequencies at various nozzle lip thickness and jet temperature were found to agree well with experiments; (5) The simulated data were used to explain, for the first time, why there are two axisymmetric screech modes and two helical/flapping screech modes; (6) The simulated data were used to show that when two tones are observed, they co-exist rather than switching from one mode to the other, back and forth, as some previous investigators have suggested; and (7) Some resources of the grant were used to support the development of new computational aeroacoustics (CAA) methodology. (Our screech tone simulations have benefited because of the availability of these improved methods.)

Tam, Christopher K. W.↗
NASA NTRS1994

Dynamic Load Balancing For Grid Partitioning on a SP-2 Multiprocessor: A Framework

Computational requirements of full scale computational fluid dynamics change as computation progresses on a parallel machine. The change in computational intensity causes workload imbalance of processors, which in turn requires a large amount of data movement at runtime. If parallel CFD is to be successful on a parallel or massively parallel machine, balancing of the runtime load is indispensable. Here a framework is presented for dynamic load balancing for CFD applications, called Jove. One processor is designated as a decision maker Jove while others are assigned to computational fluid dynamics. Processors running CFD send flags to Jove in a predetermined number of iterations to initiate load balancing. Jove starts working on load balancing while other processors continue working with the current data and load distribution. Jove goes through several steps to decide if the new data should be taken, including preliminary evaluate, partition, processor reassignment, cost evaluation, and decision. Jove running on a single IBM SP2 node has been completely implemented. Preliminary experimental results show that the Jove approach to dynamic load balancing can be effective for full scale grid partitioning on the target machine IBM SP2.

Sohn, Andrew↗
NASA NTRS1994

Dynamic Load Balancing for Grid Partitioning on a SP-2 Multiprocessor: A Framework

Computational requirements of full scale computational fluid dynamics change as computation progresses on a parallel machine. The change in computational intensity causes workload imbalance of processors, which in turn requires a large amount of data movement at runtime. If parallel CFD is to be successful on a parallel or massively parallel machine, balancing of the runtime load is indispensable. Here a framework is presented for dynamic load balancing for CFD applications, called Jove. One processor is designated as a decision maker Jove while others are assigned to computational fluid dynamics. Processors running CFD send flags to Jove in a predetermined number of iterations to initiate load balancing. Jove starts working on load balancing while other processors continue working with the current data and load distribution. Jove goes through several steps to decide if the new data should be taken, including preliminary evaluate, partition, processor reassignment, cost evaluation, and decision. Jove running on a single EBM SP2 node has been completely implemented. Preliminary experimental results show that the Jove approach to dynamic load balancing can be effective for full scale grid partitioning on the target machine IBM SP2.

Sohn, Andrew↗
NASA NTRS1994

Dynamic Load Balancing for Finite Element Calculations on Parallel Computers

Computational requirements of full scale computational fluid dynamics change as computation progresses on a parallel machine. The change in computational intensity causes workload imbalance of processors, which in turn requires a large amount of data movement at runtime. If parallel CFD is to be successful on a parallel or massively parallel machine, balancing of the runtime load is indispensable. Here a frame work is presented for dynamic load balancing for CFD applications, called Jove. One processor is designated as a decision maker Jove while others are assigned to computational fluid dynamics. Processors running CFD send flags to Jove in a predetermined number of iterations to initiate load balancing. Jove starts working on load balancing while other processors continue working with the current data and load distribution. Jove goes through several steps to decide if the new data should be taken, including preliminary evaluate, partition, processor reassignment, cost evaluation, and decision. Jove running on a single SP2 node has been completely implemented. Preliminary experimental results show that the Jove approach to dynamic load balancing can be effective for full scale grid partitioning on the target machine SP2.

Pramono, Eddy↗
DOE OSTI2019

CMS Computing Resources: Meeting the demands of the high-luminosity LHC physics program

The high-luminosity program has seen numerous extrapolations of its needed computing resources that each indicate the need for substantial changes if the desired HL-LHC physics program is to be supported within the current level of computing resource budgets. Drivers include large increases in event complexity (leading to increased processing time and analysis data size) and trigger rates needed (5-10 fold increases) for the HL-LHC program. The CMS experiment has recently undertaken an effort to merge the ideas behind short-term and long-term resource models in order to make easier and more reliable extrapolations to future needs. Near term computing resource estimation requirements depend on numerous parameters: LHC uptime and beam intensities; detector and online trigger performance; software performance; analysis data requirements; data access, management, and retention policies; site characteristics; and network performance. Longer term modeling is affected by the same characteristics, but with much larger uncertainties that must be considered to understand the most interesting handles for increasing the "physics per computing dollar" of the HL-LHC. In this presentation, we discuss the current status of long term modeling of the CMS computing resource needs for HL-LHC with emphasis on techniques for extrapolations, uncertainty quantification, and model results. We illustrate potential ways that high-luminosity CMS could accomplish its desired physics program within today's computing budgets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

AUTONOMOUS GAUSSIAN DECOMPOSITION

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Software For Computing Image Ratios

RATIO_TOOL is interactive computer program for viewing and analyzing large sets of multispectral image data created by imaging spectrometer. Uses ratios between intensities in different spectral bands in order to spot significant areas of interest within multispectral image. Each image band viewed iteratively, or selected image band of set of data requested and displayed. When image ratios computed, result displayed as grayscale image. Written in C Language.

Yates, Gigi L.↗