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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Computational Performance of Progressive Damage Analysis of Composite Laminates using Abaqus/Explicit with 16 to 512 CPU Cores

The computational scaling performance of progressive damage analysis using Abaqus/ Explicit is evaluated and quantified using from 16 to 512 CPU cores. Several analyses were conducted on varying numbers of cores to determine the scalability of the code on five NASA high performance computing systems. Two finite element models representative of typical models used for progressive damage analysis of composite laminates were used. The results indicate a 10 to 15 times speed up scaling from 24 to 512 cores. The run times were modestly reduced with newer generations of CPU hardware. If the number of degrees of freedom is held constant with respect to the number of cores, the model size can be increased by a factor of 20, scaling from 16 to 512 cores, with the same run time. An empirical expression was derived relating run time, the number of cores, and the number of degrees of freedom. Analysis cost was examined in terms of software tokens and hardware utilization. Using additional cores reduces token usage since the computational performance increases more rapidly than the token requirement with increasing number of cores. The in- crease in hardware cost with increasing cores was found to be modest. Overall the results show relatively good scalability of the Abaqus/Explicit code on up to 512 cores.

Bergan, A. C.↗
NASA NTRS2003

P2P Technology for High-Performance Computing: An Overview

The transition from cluster computing to peer-to-peer (P2P) high-performance computing has recently attracted the attention of the computer science community. It has been recognized that existing local networks and dedicated clusters of headless workstations can serve as inexpensive yet powerful virtual supercomputers. It has also been recognized that the vast number of lower-end computers connected to the Internet stay idle for as long as 90% of the time. The growing speed of Internet connections and the high availability of free CPU time encourage exploration of the possibility to use the whole Internet rather than local clusters as massively parallel yet almost freely available P2P supercomputer. As a part of a larger project on P2P high-performance computing, it has been my goal to compile an overview of the 2P2 paradigm. I have studied various P2P platforms and I have compiled systematic brief descriptions of their most important characteristics. I have also experimented and obtained hands-on experience with selected P2P platforms focusing on those that seem promising with respect to P2P high-performance computing. I have also compiled relevant literature and web references. I have prepared a draft technical report and I have summarized my findings in a poster paper.

Follen, Gregory J.↗
NASA NTRS1984

Elliptical Orbit Performance Computer Program

Elliptical Orbit Performance (ELOPE) computer program for analyzing orbital performance of space boosters uses orbit insertion data obtained from trajectory simulation to generate parametric data on apogee and perigee altitudes as function of payload data. Data used to generate presentation plots that display elliptical orbit performance capability of space booster.

Myler, T.↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data Center Immersion Cooling: A Case Study and Summary of High-Performance Computing Cooling Technologies

The future of High Performance Computing (HPC) is carved out for all high-performance facilities. As computer processing increases exponentially, especially with the demand for AI training, more speed will equate to more computing density and ultimately more power draw and resource demand. Sandia National Laboratories has not seen or known of anything that would lead us to think this might change in the next 5 to 10 years.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Scalable High Performance Computing: Direct and Large-Eddy Turbulent Flow Simulations Using Massively Parallel Computers

This final report contains reports of research related to the tasks "Scalable High Performance Computing: Direct and Lark-Eddy Turbulent FLow Simulations Using Massively Parallel Computers" and "Devleop High-Performance Time-Domain Computational Electromagnetics Capability for RCS Prediction, Wave Propagation in Dispersive Media, and Dual-Use Applications. The discussion of Scalable High Performance Computing reports on three objectives: validate, access scalability, and apply two parallel flow solvers for three-dimensional Navier-Stokes flows; develop and validate a high-order parallel solver for Direct Numerical Simulations (DNS) and Large Eddy Simulation (LES) problems; and Investigate and develop a high-order Reynolds averaged Navier-Stokes turbulence model. The discussion of High-Performance Time-Domain Computational Electromagnetics reports on five objectives: enhancement of an electromagnetics code (CHARGE) to be able to effectively model antenna problems; utilize lessons learned in high-order/spectral solution of swirling 3D jets to apply to solving electromagnetics project; transition a high-order fluids code, FDL3DI, to be able to solve Maxwell's Equations using compact-differencing; develop and demonstrate improved radiation absorbing boundary conditions for high-order CEM; and extend high-order CEM solver to address variable material properties. The report also contains a review of work done by the systems engineer.

Morgan, Philip E.↗
NASA NTRS1994

High Performance Computing at NASA

The speaker will give an overview of high performance computing in the U.S. in general and within NASA in particular, including a description of the recently signed NASA-IBM cooperative agreement. The latest performance figures of various parallel systems on the NAS Parallel Benchmarks will be presented. The speaker was one of the authors of the NAS (National Aerospace Standards) Parallel Benchmarks, which are now widely cited in the industry as a measure of sustained performance on realistic high-end scientific applications. It will be shown that significant progress has been made by the highly parallel supercomputer industry during the past year or so, with several new systems, based on high-performance RISC processors, that now deliver superior performance per dollar compared to conventional supercomputers. Various pitfalls in reporting performance will be discussed. The speaker will then conclude by assessing the general state of the high performance computing field.

Bailey, David H.↗
DOE OSTI2024

Transforming Energy Through Computational Excellence: NREL HPC Resources for High Performance Computing for Energy Innovation (HPC4EI) Program

NREL hosts computing facilities for the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In 2024, NREL introduced Kestrel, the 3rd generation, EERE-sponsored supercomputer dedicated to renewable energy and energy efficiency research. Kestrel has already been used for hundreds of research projects by NREL, other national laboratories, and university partners. This includes HPC4EI-sponsored industrial partnerships.

high-performance computing↗
DOE OSTI2024

Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

47 OTHER INSTRUMENTATION↗
NASA NTRS1991

Lanczos eigensolution method for high-performance computers

The theory, computational analysis, and applications are presented of a Lanczos algorithm on high performance computers. The computationally intensive steps of the algorithm are identified as: the matrix factorization, the forward/backward equation solution, and the matrix vector multiples. These computational steps are optimized to exploit the vector and parallel capabilities of high performance computers. The savings in computational time from applying optimization techniques such as: variable band and sparse data storage and access, loop unrolling, use of local memory, and compiler directives are presented. Two large scale structural analysis applications are described: the buckling of a composite blade stiffened panel with a cutout, and the vibration analysis of a high speed civil transport. The sequential computational time for the panel problem executed on a CONVEX computer of 181.6 seconds was decreased to 14.1 seconds with the optimized vector algorithm. The best computational time of 23 seconds for the transport problem with 17,000 degs of freedom was on the the Cray-YMP using an average of 3.63 processors.

Bostic, Susan W.↗
NASA NTRS1994

Computational Aeroscience at NAS and Future Trends in High Performance Computing

NASA's Numerical Aerodynamic Simulation (NAS) program provides a unique world class supercomputing capability which is readily accessed by the U.S. aeronautics community and offers them the opportunity to perform high speed computations and simulations for a broad range of aerospace research applications. In addition, the NAS program performs on-going research and advanced technology development to ensure the innovative application of newly emerging technologies to computational fluid dynamics and other important aeroscience disciplines. The NAS system and its interaction with industry are described. Furthermore, the results of an analysis of current and future technologies and their potential impact on high performance computing are also presented.

Cooper, David M.↗
DOE OSTI2024

Microgrid Integration with High Performance Computing Systems for Microreactor Operation

Multiple nuclear microreactor concepts are currently being developed across several sizes and fuel types with high performance computing (HPC) systems anticipated to be end-users of the power. Nuclear microreactors are small in size, portable, produce less than 10 MW electric, operate autonomously, and have a refueling interval of as many as 10 years. However, their load-follow is also generally limited to 10%/minute or worse whereas the power variance in HPC systems easily exceeds this constraint under normal operations. This study explores an approach that requires no load-follow from the microreactor but integrates the HPC system with a microgrid built from commercial-off-the-shelf components. Three typical HPC architectures are explored in the context of microgrid operation in this study. Components of power quality and transient response are empirically measured for five different HPC load-follow response levels using a self-contained mobile datacenter connected to the microgrid capable of integration with a nuclear microreactor.

microgrids↗
NASA NTRS2000

Welcome to the NASA High Performance Computing and Communications Computational Aerosciences (CAS) Workshop 2000

The purpose of the CAS workshop is to bring together NASA's scientists and engineers and their counterparts in industry, other government agencies, and academia working in the Computational Aerosciences and related fields. This workshop is part of the technology transfer plan of the NASA High Performance Computing and Communications (HPCC) Program. Specific objectives of the CAS workshop are to: (1) communicate the goals and objectives of HPCC and CAS, (2) promote and disseminate CAS technology within the appropriate technical communities, including NASA, industry, academia, and other government labs, (3) help promote synergy among CAS and other HPCC scientists, and (4) permit feedback from peer researchers on issues facing High Performance Computing in general and the CAS project in particular. This year we had a number of exciting presentations in the traditional aeronautics, aerospace sciences, and high-end computing areas and in the less familiar (to many of us affiliated with CAS) earth science, space science, and revolutionary computing areas. Presentations of more than 40 high quality papers were organized into ten sessions and presented over the three-day workshop. The proceedings are organized here for easy access: by author, title and topic.

Schulbach, Catherine H.↗
DOE OSTI2025

Simulation of Physics-Based 0-10Hz Strong Motion Using High Performance Computing Supporting Refinements to Regional Ground Motion Models for the Central Eastern US

In collaboration with the U.S. Nuclear Regulatory Commission (NRC) the LLNL has developed a computationally efficient simulation platform designed to perform physics-based ground motion simulations for crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using high-performance computing. The main objective of the earthquake simulations was to use synthetic ground motion to provide constrains to refinements of existing ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-10Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model (1994). In simulations performed during the first stage of this project the Saikia’s velocity model demonstrated better performance in modelling high frequency regional wave propagation for the CEUS region recorded during the Mw5.0 November 7, 2016, Cushing Oklahoma (Taylor et al., 2017), and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes. The proposed regional 3D model includes random perturbations to the 1D background model using the stochastic scheme of Pitarka and Mellors (2021). In addition, validation analysis of the rupture generator and regional wave propagation models, using comparisons with different GMMs for Mw6.5 and Mw7.0 scenario earthquakes in the CEUS region resulted in a very good match between the simulated and empirical ground motion models. For the purposes of seismic hazard assessment at the existing and planned nuclear power plants, NRC is interested in studies aimed at improving the current ground motion models (GMM) for both Stable Continental Regions (SCR) in the Central and Eastern US and Active Crustal Regions (ACR) in the Western US. Due to lack of recorded data, these improvements require synthetic data for short fault distances and large magnitude earthquakes for which the existing recorded data is not enough to uniquely constrain the GMMs. The need for simulations and strong motion data is especially critical for the CEUS region where we do not have recorded data from potentially large damaging earthquakes with moment magnitudes 6.0 and higher. In this the project, we focused on 10Hz simulations of Mw7.0 scenario earthquakes with strike slip and thrust faulting mechanisms. We used more than 50 Mw7.0 earthquake rupture scenarios to investigate the ground motion uncertainty due to unknown earthquake rupture parameters, in particular, the slip distribution, rupture velocity, and faulting mechanism, and their implication on ground motion amplification due to forward rupture directivity effects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗