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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FacultyHack Events: Faculty-Focused Hackathons for High-Performance Computing Curriculum Development

Broadening participation initiatives are important for engaging underrepresented groups in science, technology, engineering, and math (STEM). Such initiatives help foster supportive and inclusive work environments that promote creativity and productivity. While there are initiatives that aim to engage students and faculty, opportunities remain to improve faculty support. Hackathons have proved to be a useful approach for student engagement. There are, however, limited insights into whether and how such events would also work for faculty aiming to develop curricula. This paper discusses the design of a faculty-focused hackathon event, FacultyHack, for curriculum development. We outline the logistics and structure for two past FacultyHack events, detail changes between events, and describe potential improvements and lessons learned.

Holmen, John [ORNL] (ORCID:0000000259342641)↗
DOE OSTI2018

High Performance Computing Cluster Engineering Academy

Collection of tools and code for evaluation testing of HPC orchestration solutions by HPCCEA summer students. These scripts and configuration files were written by HPCCEA summer students as part of their summer internship where they explore alternative solutions to our current HPC cluster management software. These scripts/configuration files range from using configuration management tools (Puppet, Chef, Ansible, Fabric, etc.) to scripting languages (Phython, BASH, Perl, etc.). Each year, new students will be added to the project and expand on previous years work.

Bennett, ThomasG↗
DOE OSTI2020

EspressoDB: A scientific database for managing high-performance computing workflows

EspressoDB is a programmatic object-relational mapping (ORM) data management framework implemented in Python and based on the Django web framework. EspressoDB was developed to streamline data management, centralize and promote data integrity, while providing domain flexibility and ease of use. It is designed to directly integrate in utilized software to allow dynamical access to vast amount of relational data at runtime.

Chang, Chia↗
DOE OSTI2016

Analysis of Alternatives (AoA) of Open Colllaboration and Research Capabilities Collaboratipon in Research and Engineering in Advanced Technology and Education and High-Performance Computing Innovation Center (HPCIC) on the LVOC.

The Livermore Valley Open Campus (LVOC), a joint initiative of the National Nuclear Security Administration (NNSA), Lawrence Livermore National Laboratory (LLNL), and Sandia National Laboratories (SNL), enhances the national security missions of NNSA by promoting greater collaboration between world-class scientists at the national security laboratories, and their partners in industry and academia. Strengthening the science, technology, and engineering (ST&E) base of our nation is one of the NNSA’s top goals. By conducting coordinated and collaborative programs, LVOC enhances both the NNSA and the broader national science and technology base, and helps to ensure the health of core capabilities at LLNL and SNL. These capabilities must remain strong to enable the laboratories to execute their primary mission for NNSA.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Validation of MCNP6 Version 1.0 with the ENDF/B-VII.1 Cross Section Library for Uranium Metal, Oxide, and Solution Systems on the High Performance Computing Platform Moonlight

In this document, the code MCNP is validated with ENDF/B-VII.1 cross section data under the purview of ANSI/ANS-8.24-2007, for use with uranium systems. MCNP is a computer code based on Monte Carlo transport methods. While MCNP has wide reading capability in nuclear transport simulation, this validation is limited to the functionality related to neutron transport and calculation of criticality parameters such as k eff .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Create full-scale predictive economic models on ROI and innovation with performance computing

The U.S. Department of Energy (DOE), the world's largest buyer and user of supercomputers, awarded IDC Research, Inc. a grant to create two macroeconomic models capable of quantifying, respectively, financial and non-financial (innovation) returns on investments in HPC resources. Following a 2013 pilot study in which we created the models and tested them on about 200 real-world HPC cases, DOE authorized us to conduct a full-out, three-year grant study to collect and measure many more examples, a process that would also subject the methodology to further testing and validation. A secondary, "stretch" goal of the full-out study was to advance the methodology from association toward (but not all the way to) causation, by eliminating the effects of some of the other factors that might be contributing, along with HPC investments, to the returns produced in the investigated projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optimization and Control of Burning Plasmas Through High Performance Computing

This project has revived the FACETS code, that has been developed under SciDAC fund- ing in 2008-2012. The code has been dormant for a number of years after the SciDAC funding stopped. FACETS depends on external packages. The external packages and libraries such as PETSc, FFTW, HDF5 and NETCDF that are included in FACETS have evolved during these years. Some packages in FACETS are also parts of other codes such as PlasmaState, NUBEAM, GACODES, and UEDGE. These packages have been also evolved together with their host codes which include TRANSP, TGYRO and XPTOR. Finally, there is also a set of packages in FACETS that are being developed and maintained by Tech-X. These packages include BILDER, SciMake, and FcioWrappers. Many of these packages evolved significantly during the last several years and FACETS had to be updated to synchronize with the re- cent progress in the external packages. The PI has introduced new changes to the BILDER package to support the updated interfaces to the external modules. During the last year of the project, the FACETS version of the UEDGE code has been extracted from FACETS as a standalone package. The PI collaborates with the scientists from LLNL on the updated UEDGE model in FACETS. Drs. T. Rognlien, M. Umansky and A. Dimits from LLNL are contributing to this task.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

High Performance Computing Tools to Advance Materials Joining Technology

In this project, an in-house HPC finite element code was developed with novel computational schemes that provided fast and high-fidelity simulation capability for welding processes and allied manufacturing processes such as metal additive manufacturing processes. Benchmark comparison confirmed that, for the testing cases studied, the newly developed in-house code resulted in increases in computational speed in the ranges of 100x to 1000x depending on the size and complexity of the testing cases, when compared to today’s commercial welding process simulation tools. As demonstrated by the selected examples in this report, the breakthrough increases in computational simulation speeds achieved by the in-house HPC code means that high fidelity simulations of certain complex welding problems that would require months even years of computational time with today’s commercial code could be solved in two to three days. Similar order of magnitudes of reduction in computational times for metal additive manufacturing process were also achieved.

36 MATERIALS SCIENCE↗
DOE OSTI2019

High Performance Computing for Phase Predictions for Multi – Component Alloy Systems (Final CRADA Report)

The goal of this work is to establish important alloy new data from rigorous, first-principles-based computational approaches, for systems that presently lack critical data. The focus is on lightweight alloys due to their importance in energy-efficient applications for transportation and other areas, particularly for alloy compositions such as Al-Li-Ti where existing data is limited. By providing input on this class of compositions, we seek to significantly reduce the effort, expense and energy of experimental explorations of these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Quantum Optimal Control Using High Performance Computing

This feasibility study developed numerical methods for the optimal control problem for realizing logical gates in closed quantum systems, where the evolution of the state vector is governed by the time dependent Schrodinger equation. The number of parameters in the control functions is made independent of the number of time steps by expanding them in terms of B-spline basis functions, with and without carrier waves. We use an interior point gradient-based technique from the IPOPT package to minimize the gate infidelity subject to amplitude constraints on the control functions. The symplectic Stromer-Verlet scheme is used to integrate a real-valued formulation of Schrodinger's equation in time and the gradient of the gate infidelity is obtained by solving the corresponding adjoint equation. This allows all components of the gradient to be calculated at the cost of solving three Schrodinger systems, independently of the number of parameters in the control functions. The correctness of the method is verified and applied to Hamiltonians that model the dynamics of coupled super-conducting qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗