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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Completion processing for data communications instructions

Completion processing of data communications instructions in a distributed computing environment with computers coupled for data communications through communications adapters and an active messaging interface (`AMI`), injecting for data communications instructions into slots in an injection FIFO buffer a transfer descriptor, at least some of the instructions specifying callback functions; injecting a completion descriptor for each instruction that specifies a callback function into an injection FIFO buffer slot having a corresponding slot in a pending callback list; listing in the pending callback list callback functions specified by data communications instructions; processing each descriptor in the injection FIFO buffer, setting a bit in a completion bit mask corresponding to the slot in the FIFO where the completion descriptor was injected; and calling by the AMI any callback functions in the pending callback list as indicated by set bits in the completion bit mask.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

The Science Training Program for Young Italian Physicists and Engineers at Fermilab

Since 1984 Fermilab has been hosting a two-month summer training program for selected undergraduate and graduate Italian students in physics and engineering. Building on the traditional close collaboration between the Italian National Institute of Nuclear Physics (INFN) and Fermilab, the program is supported by INFN, by the DOE and by the Scuola Superiore di Sant`Anna of Pisa (SSSA), and is run by the Cultural Association of Italians at Fermilab (CAIF). This year the University of Pisa has qualified it as a “University of Pisa Summer School”, and will grant successful students with European Supplementary Credits. Physics students join the Fermilab HEP research groups, while engineers join the Particle Physics, Accelerator, Technical, and Computing Divisions. Some students have also been sent to other U.S. laboratories and universities for special trainings. The programs cover topics of great interest for science and for social applications in general, like advanced computing, distributed data analysis, nanoelectronics, particle detectors for earth and space experiments, high precision mechanics, applied superconductivity. In the years, over 350 students have been trained and are now employed in the most diverse fields in Italy, Europe, and the U.S. In addition, the existing Laurea Program in Fermilab Technical Division was extended to the whole laboratory, with presently two students in Master’s thesis programs on neutrino physics and detectors in the Neutrino Division. And finally, a joint venture with the Italian Scientists and Scholars North-America Foundation (ISSNAF) provided this year 4 professional engineers free of charge for Fermilab. More details on all of the above can be found below.

Barzi, Emanuela↗
DOE OSTI2016

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than Grid computing can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, Europe and Russia (in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF), MIRA supercomputer at Argonne Leadership Computing Facilities (ALCF), Supercomputer at the National Research Center Kurchatov Institute , IT4 in Ostrava and others). Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full production for the ATLAS experiment since September 2015. We will present our current accomplishments with running PanDA WMS at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.

Data-intensive science↗
DOE OSTI2018

Mitigating component performance variation

Apparatus and methods may provide for characterizing a plurality of similar components of a distributed computing system based on a maximum safe operation level associated with each component and storing characterization data in a database and allocating non-uniform power to each similar component based at least in part on the characterization data in the database to substantially equalize performance of the components.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Containerization in ATLAS Software Development and Data Production

The ATLAS experiment's software production and distribution on the grid benefits from a semi-automated infrastructure that provides up-to-date information about software usability and availability through the CVMFS dis-tribution service for all relevant systems. The software development process uses a Continuous Integration pipeline involving testing, validation, packag-ing and installation steps. For opportunistic sites that can not access CVMFS, containerized releases are needed. These standalone containers are currently created manually to support Monte-Carlo data production at such sites. In this paper we will describe an automated procedure for the containerization of AT-LAS software releases in the existing software development infrastructure, its motivation, integration and testing in the distributed computing system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integration of Rucio in Belle II

The Belle II experiment, which started taking physics data in April 2019, will multiply the volume of data currently stored on its nearly 30 storage elements worldwide by one order of magnitude to reach about 340 PB of data (raw and Monte Carlo simulation data) by the end of operations. To tackle this massive increase and to manage the data even after the end of the data taking, it was decided to move the Distributed Data Management software from a homegrown piece of software to a widely used Data Management solution in HEP and beyond : Rucio. This contribution describes the work done to integrate Rucio with Belle II distributed computing infrastructure as well as the migration strategy that was successfully performed to ensure a smooth transition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Modularized Urban Scale Building Energy Modeling Framework Designed with An Open Mind

In recent years, physics-based building energy modeling (BEM) has started being used to evaluate the performance of buildings in the context of connected communities and on an urban scale to study their aggregated energy use, interactions, and impacts on the energy supply infrastructure and environment. The development of urban-scale BEM solutions needs extensive effort. Existing attempts tend to focus on different aspects of BEM on an urban scale, such as collecting as-built building data from different information sources, integrating geometry modeling with geographic information systems (GISs), representing operational and occupancy profiles, automating workflow, processing and visualizing the results, and conducting large-scale simulations. Urban-scale BEM development would benefit from multi-disciplinary research areas and from an open platform to adopt advancements on data sources and tools. For these purposes, this research proposes a modularized bottom-up model creation and simulation framework that is built on the state-of-the-art BEM tools and can accommodate different building stock data. This framework uses a standardized schema to describe building design and operational characteristics, and it can be instantiated from different building survey datasets with heterogeneous structures. The paper demonstrates how thousands of surveyed buildings from the 2012 U.S. Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) were one-to-one converted to EnergyPlus models through the schema and the model generation process, then simulated with distributed computing, and their results are summarized.

Lei, Xuechen↗
DOE OSTI2018

Use of Physics Based Management Optimization for Groundwater Remedy Design - 18671

Numerical modeling coupled with formal mathematical optimization techniques was used to design a groundwater remedy strategy to address a carbon tetrachloride (CCl{sub 4}) plume covering several square miles. The optimization approach, methodology, and project costs are described. The site consists of a large grain elevator in Nebraska. CCl{sub 4} was used as a fumigant and subsequently impacted soils and groundwater. Remedy design focused on the more diffuse plume, which extended approximately 4.5 miles down gradient of the source area. An eleven-layer model was developed and calibrated to observed groundwater conditions. This model was linked with formal optimization techniques to identify the best design capable of meeting client specified requirements and well location limitations. The planned system included requirements on the location of extraction and injection wells, total system flow rate, number of management periods, and specific constraints limiting further plume expansion. A separate trinitrotoluene (TNT) contaminant plume from a third party was located vertically above the leading edge of the CCl{sub 4} plume within the same aquifer. A further constraint imposed was that the design could not affect or cause additional migration of the TNT plume. Optimal system design was determined using HydroGeoLogic's Physics Based Management Optimization (PBMO{sup TM}) system. Over 38,000 candidate solutions consisting of different combinations of pumping and injection wells locations and rates, were evaluated through flow modeling coupled with either particle tracking or fully convective-dispersive transport simulations to determine the optimal design. Planning for installation of the optimal design is underway. Simulations were completed on a distributed computing platform consisting of 3 Quad Core PCs. Flow and particle tracking simulations required less than 2 minutes per simulation. Coupled flow and transport simulation run times were generally between 30-40 minutes per simulation. The total costs for developing the groundwater flow model and completing the subsequent optimal design were approximately $400,000. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Editorial: Edge computation and digital distribution networks

The development of digital technologies is penetrating all areas of energy revolution. Based on the in-depth integration of advanced digital technologies, distribution networks are gradually transforming into digital distribution networks (DDNs) with tremendous changes from the structure to the operation mode. DDNs is the digitalized appearance of the physical distribution network, in which ubiquitous connections and massive data are the basic characteristics (Huo et al., 2022). It is an important task to utilize the massive data and propose novel operation modes to construct more efficient and intelligent distribution networks (Jian et al., 2022). Among the advanced digital technologies in DDNs, edge computing has received wide attention (Zhao et al., 2022). It has superior performance in local sensing and intelligent computation, which can effectively relieve huge communication pressure. However, the limited computing resources and the complex computing tasks at the edge side significantly challenge the collaboration of distribution network regulation and advanced digital technologies (Hu et al., 2022). It is necessary to find out proper methods to utilize advanced digital technology to construct DDNs. This Research Topic is organized to introduce the recent progress in the construction, operation and advanced computational methods for DDNs. Finally, seven papers have been accepted, which can be sorted into the following three categories: 1) Evolution and technical features of DDNs, 2) Intelligent operation control of DDNs, 3) Advanced simulation for large-scale DDNs. The three sections below respectively introduce the major research and contributions of the papers covered in each category.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Assessment of Local Observation of Atomic Ordering in Alloys via the Radial Distribution Function: A Computational and Experimental Approach

As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni 3 Al. The ability to observe a signal of atomic ordering consistent with the known L1 2 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al 1.3 CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hierarchical power distribution in large scale computing systems

Techniques for managing power distribution amongst processors in a massively parallel computer architecture are disclosed. The techniques utilize a hierarchy that organizes the various processors of the massively parallel computer architecture. The hierarchy groups numbers of the processors at the lowest level. When processors complete tasks, the power assigned to those processors is distributed to other processors in the same group so that the performance of those processors can be increased. Hierarchical organization simplifies the calculations required for determining how and when to distribute power, because when tasks are complete and power is available for distribution, a relatively small number of processors are available for consideration to receive that power. The number of processors that are grouped together can be adjusted in real time based on performance factors to improve the trade-off between calculation speed and power distribution efficacy.

42 ENGINEERING↗
DOE OSTI2012

Defect level distributions and atomic relaxations induced by charge trapping in amorphous silica

We compute the distribution of electronic levels of native defects in amorphous silica from total energy differences of charge-state density functional theory calculations over an ensemble of atomic structures. The predicted distributions reproduce results from trap spectroscopy by charge injection experiments, validating the calculations. Furthermore, our study characterizes the experimentally inaccessible contributions of individual defect types to the overall distribution. Computed electron and hole trapping levels provide insight into the positive charge buildup in bulk silica observed in negative-bias-temperature-instability, an important degradation mechanism of metal-oxide-semiconductor devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Robust Distributed State Estimator for Interconnected Transmission and Distribution Networks (Final Report RPPR-1)

This project’s objective is to develop a combined transmission and distribution state estimator which accounts for very large system size and model complexity (by way of distributing the computations) and large number of solar PV units connected to the distribution system on multiple feeders. The project not only provides a robust formulation and solution to this problem but also tests the solution by implementing it on a well-established large utility system. It introduces several improvements with respect to the state of the art in existing state estimation software: (a) The developed state estimator (SE) allows robust and accurate monitoring of bidirectional flows in distribution systems which result due to the distributed energy sources which are not observable and thus not incorporated in generation dispatch; (b) Large utility systems with tens of thousands of transmission buses and hundreds of thousands of distribution nodes are difficult to model as a single integrated system. This shortcoming is addressed by developing a “scalable distributed computational framework” which allows splitting the ultra large system models into several small subsystems and coordinating their solution by a robust and practical state estimation formulation; (c) Measurement errors irrespective of their locations are detected and removed by the developed state estimator. Historically, transmission and distribution systems were analyzed and operated as two independent systems. Given the non-transposed short feeder sections, unevenly loaded phases, strictly radial configuration and unidirectional power flows in the absence of remote generation, distribution system analysis was customized to account for these characteristics. However, some of these assumptions are no longer valid (non-radial configuration, bidirectional power flows) and thus distribution system analysis should be revisited. Furthermore, in the past, the interaction between the transmission and distribution systems was quite passive, where distribution substations were modeled as lumped loads in the transmission system model. With substantial generation injected by renewable generation located in the distribution systems, such modeling will no longer be accurate. The developed state estimator facilitates proper monitoring of the interactions between the transmission and distribution systems and enables smart dispatch of these units which are made observable by the state estimator.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

A Distributed Python HPC Framework: ODIN, PyTrilinos, & Seamless

Under this grant, three significant software packages were developed or improved, all with the goal of improving the ease-of-use of HPC libraries. The first component is a Python package, named DistArray (originally named Odin), that provides a high-level interface to distributed array computing. This interface is based on the popular and widely used NumPy package and is integrated with the IPython project for enhanced interactive parallel distributed computing. The second Python package is the Distributed Array Protocol (DAP) that enables separate distributed array libraries to share arrays efficiently without copying or sending messages. If a distributed array library supports the DAP, it is then automatically able to communicate with any other library that also supports the protocol. This protocol allows DistArray to communicate with the Trilinos library via PyTrilinos, which was also enhanced during this project. A third package, PyTrilinos, was extended to support distributed structured arrays (in addition to the unstructured arrays of its original design), allow more flexible distributed arrays (i.e., the restriction to double precision data was lifted), and implement the DAP. DAP support includes both exporting the protocol so that external packages can use distributed Trilinos data structures, and importing the protocol so that PyTrilinos can work with distributed data from external packages.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-J-204: Can a Commercial System for MR-IGRT Be Used to Treat Patients Without Acquiring a CT Scan?

Patients treated using a magnetic-resonance image guided radiation therapy (MR-IGRT) system received both CT and MR simulations. During planning, the CT is used to determine relative electron density (RED) using a calibration table. This study aims to investigate the feasibility of MR-only treatments by comparing CT-computed dose distributions to those computed with combinations of water (1.0), lung (0.26), tissue (1.02), and bone (1.12) bulk RED overrides, and to identify the effects of the magnetic field on the RED-overridden doses. Methods: Four patients who received treatment using a commercial MR-IGRT system were analyzed (1 lung, 2 abdomen, and 1 pelvis). The clinical plans were computed using the first fraction MRI as primary, and the simulation CT as secondary for REDs. Plans were reoptimized using default bulk RED overrides (water/lung and tissue/lung for the lung patient, water/bone, tissue/bone, water only, and tissue only for the abdomen and pelvis patients). Additionally, each plan was re-optimized to include the static magnetic field. All plans were normalized to the same PTV coverage as the clinical plan. Dose-difference volumes and DVHs were computed for bulk density override plans, and 3D gamma analyses between each plan and its accompanying magnetic field plan were performed using 3%/3 mm dose difference and distance-to-agreement criteria using the PTV and Skin as masking structures. Results: The average differences in PTV and organs-at-risk mean dose for all RED combinations tested were −0.19 Gy (−0.62 – 0.06 Gy) and −0.34 Gy (−1.76 – 0.33 Gy), respectively. The average PTV and Skin gamma pass rates for all RED combinations tested were 99.88% (99.5% – 100%) and 98. 35% (96.3% – 99.6%). No systematic differences in DVHs or isodoses were observed. Conclusions: It is likely that that a commercial MR-IGRT system may produce high quality treatment plans without the need for CT scans. Authors of this abstract are members of the Washington University Radiation Oncology department, which has a research agreement with ViewRay, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Uncertainty quantification of microstructural properties due to variability in measured pole figures

Experimental pole figures are an important input for microstructure homogenization models. In this paper, we derive an exact analytical formulation to quantify the uncertainties in homogenized properties due to uncertainty in the experimentally measured pole figures. The pole figures are acquired from a set of Ti-7Al alloy samples. These samples were obtained from the same process: by compressing a beta forged ingot at room temperature followed by annealing. The samples were taken from different regions of the original ingot, and this created variability in the resulting pole figures. The joint multivariate probability distributions of the computed orientation distribution function (ODF) is then found using the method of characteristic functions based on a Gaussian model of the pole figures. Engineering properties such as elastic modulus can be obtained by volume averaging over the ODF. We also show that uncertainty in elastic properties can be analytically obtained using direct transformation of random variables in the homogenization approach.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Computation of parton distributions from the quasi-PDF approach at the physical point

We show the first results for parton distribution functions within the proton at the physical pion mass, employing the method of quasi-distributions. In particular, we present the matrix elements for the iso-vector combination of the unpolarized, helicity and transversity quasi-distributions, obtained with $N_f$ = 2 twisted mass cloverimproved fermions and a proton boosted with momentum |$\widehat{p}$| = 0.83 GeV. The momentum smearing technique has been applied to improve the overlap with the proton boosted state. Moreover, we present the renormalized helicity matrix elements in the RI’ scheme, following the non-perturbative renormalization prescription recently developed by our group.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗