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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ion and Electron Acoustic Bursts during Anti-Parallel Reconnection Driven by Lasers

Magnetic reconnection converts magnetic energy into thermal and kinetic energy in plasma. Among the numerous candidate mechanisms, ion acoustic instabilities driven by the relative drift between ions and electrons (or equivalently, electric current) have been suggested to play a critical role in dissipating magnetic energy in collisionless plasmas. However, their existence and effectiveness during reconnection have not been well understood due to ion Landau damping and difficulties in resolving the Debye length scale in the laboratory. We report a sudden onset of ion acoustic bursts measured by collective Thomson scattering in the exhaust of anti-parallel magnetically driven reconnection using high-power lasers. The ion acoustic bursts are followed by electron acoustic bursts with electron heating and bulk acceleration. We reproduce these observations with one- and two-dimensional particle-in-cell simulations in which an electron outflow jet drives ion acoustic instabilities, forming double layers. These layers induce electron two-stream instabilities that generate electron acoustic bursts and energize electrons. Our results demonstrate the importance of ion and electron acoustic dynamics during reconnection when ion Landau damping is ineffective, a condition applicable to a range of astrophysical plasmas including near-Earth space, stellar flares and black hole accretion engines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Parallel Distributed Hydrology Soil Vegetation Model (DHSVM) using global arrays

The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using Global Arrays (GA). To analyze parallel performance, DHSVM was used to simulate two significantly sized river basins located in northwest continental United States and southwest Canada at 90~m resolution: (1) Clearwater (25,000~km2) and (2) Columbia (668,000~km2). Meteorological forcing applied to both basins was dynamically down-scaled from a global circulation model using the Weather Research and Forecasting (WRF) model and read into DHSVM as 2D maps each time step. We report parallel code speed up was significant. Run times for 1-year simulations were reduced by an order of magnitude for both test basins. A maximum speed up of 105 was attained with 480 processors while simulating the Columbia basin. Speed up was limited by input dominated tasks, particularly the input of meteorological forcing data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Fabrication of First 4-m Coils for the LARP MQXFA Quadrupole and Assembly in Mirror Structure

The US LHC Accelerator Research Program is constructing prototype interaction region quadrupoles as part of the US in-kind contribution to the Hi-Lumi LHC project. The low-beta MQXFA Q1/Q3 coils have a 4-m length and a 150 mm bore. The design is first validated on short, one meter models (MQXFS) developed as part of the longstanding Nb3Sn quadrupole R&D by LARP in collaboration with CERN. In parallel, facilities and tooling are being developed and refined at BNL, LBNL, and FNAL to enable long coil production, assembly, and cold testing. Long length scale-up is based on the experience from the LARP 90 mm aperture (TQ-LQ) and 120 mm aperture (HQ and Long HQ) programs. A 4-m long MQXF practice coil was fabricated, water jet cut and analyzed to verify procedures, parts, and tooling. In parallel, the first complete prototype coil (QXFP01a) was fabricated and assembled in a long magnetic mirror, MQXFPM1, to provide early feedback on coil design and fabrication following the successful experience of previous LARP mirror tests.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems

Parallel I/O hardware and software infrastructure is a key contributor to performance variability for applications running on large-scale HPC systems. This variability confounds efforts to predict application performance for characterization, modeling, optimization, and job scheduling. We propose a modeling approach that improves predictive ability by explicitly treating the variability and by leveraging the sensitivity of application parameters on performance to group applications with similar characteristics. We develop a Gaussian process-based machine learning algorithm to model I/O performance and its variability as a function of application and file system characteristics. We demonstrate the effectiveness of the proposed approach using data collected from the Edison system at the National Energy Research Scientific Computing Center. The results show that the proposed sensitivity-based models are better at prediction when compared with application-partitioned or unpartitioned models. We highlight modeling techniques that are robust to the outliers that can occur in production parallel file systems. Using the developed metrics and modeling approach, we provide insights into the file system metrics that have a significant impact on I/O performance.

I/O performance variability↗
DOE OSTI2025

Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN

We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) computations in both training scale and data diversity. It abstracts over message passing algorithms, allowing both reproduction of and comparison across algorithmic innovations that define nearest-neighbor convolution in GNNs. This work discusses a series of optimizations that have allowed scaling up the GFMs training to tens of thousands of GPUs on datasets consisting of hundreds of millions of graphs. Our GFMs use multitask learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on two state-of-the-art US Department of Energy (US-DOE) supercomputers, namely the Perlmutter petascale system at the National Energy Research Scientific Computing Center and the Frontier exascale system at Oak Ridge Leadership Computing Facility. The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2000 GPUs on Perlmutter and 16,000 GPUs on Frontier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Record acceleration of the two-dimensional Ising model using a high-performance wafer-scale engine

The versatility and wide-ranging applicability of the Ising model, originally introduced to study phase transitions in magnetic materials, have made it a cornerstone in statistical physics and a valuable tool for evaluating the performance of emerging computer hardware. Here, we present a novel implementation of the two-dimensional Ising model on Cerebras Wafer-Scale Engine (WSE) – a revolutionary processor that is opening new frontiers in computing. In our deployment of the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representation storing 16 spins on each int16 word, and efficiently distributed the spins over the processing units enabling seamless weak scaling and limiting communications to only immediate neighboring units. Our implementation can handle up to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flip attempts per second for Ising models with up to 200 million spins. This represents a gain of up to 148 times over previously reported single-devices with a highly optimized implementation on NVIDIA V100 and up to 88 times in productivity compared to NVIDIA H100. Our findings highlight the significant potential of the WSE in scientific computing, particularly in the field of materials modeling.

Ising model↗
NASA NTRS1990

Ridge belts - Evidence for regional- and local-scale deformation on the surface of Venus

Models for the formation of the ridge belt fan assemblage on Venus are evaluated through consideration of the orientation, spatial distribution, topographic expression, and wavelengths of observed tectonic surface features. A compressional mechanism is favored for long wavelength deformation corresponding to the spacing of ridge belts (300 - 400 km). However, short wavelength ridges and grooves (10 - 20 km) that are contained within belts and trend parallel to them are likely to be compressional and extensional, and to reflect both regional and local stress fields. It is hypothesized that large-scale (much greater than ridge spacing) early-stage mantle downwelling is the source of regional compression responsible for the establishment of the long wavelength of deformation.

Zuber, M. T.↗
DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

First Observations of a Foreshock Bubble at Earth: Implications for Magnetospheric Activity and Energetic Particle Acceleration

Earth?s foreshock, which is the quasi-parallel region upstream of the bow shock, is a unique plasma region capable of generating several kinds of large-scale phenomena, each of which can impact the magnetosphere resulting in global effects. Interestingly, such phenomena have also been observed at planetary foreshocks throughout our solar system. Recently, a new type of foreshock phenomena has been predicted: foreshock bubbles, which are large-scale disruptions of both the foreshock and incident solar wind plasmas that can result in global magnetospheric disturbances. Here we present unprecedented, multi-point observations of foreshock bubbles at Earth using a combination of spacecraft and ground observations primarily from the Time History of Events and Macroscale Interactions during Substorms (THEMIS) mission, and we include detailed analysis of the events? global effects on the magnetosphere and the energetic ions and electrons accelerated by them, potentially by a combination of first and second order Fermi and shock drift acceleration processes. This new phenomena should play a role in energetic particle acceleration at collisionless, quasi-parallel shocks throughout the Universe.

Turner, D. L.↗
DOE OSTI2022

FilDReaMS: II. Application to the analysis of the relative orientations between filaments and the magnetic field in four Herschel fields

Context. Both simulations and observations of the interstellar medium show that the study of the relative orientations between filamentary structures and the magnetic field can bring new insight into the role played by magnetic fields in the formation and evolution of filaments and in the process of star formation. Aims. We provide a first application of FilDReaMS, the new method presented in the companion paper to detect and analyze filaments in a given image. The method relies on a template that has the shape of a rectangular bar with variable width. Our goal is to investigate the relative orientations between the detected filaments and the magnetic field. Methods. We apply FilDReaMS to a small sample of four Herschel fields (G210, G300, G82, G202) characterized by different Galactic environments and different evolutionary stages. First, we look for the most prevalent bar widths, and we examine the networks formed by filaments of different bar widths as well as their hierarchical organization. Second, we compare the filament orientations to the magnetic field orientation inferred from Planck polarization data and, for the first time, we study the statistics of the relative orientation angle as functions of both spatial scale and H2 column density. Results. We find preferential relative orientations in the four Herschel fields: small filaments with low column densities tend to be slightly more parallel than perpendicular to the magnetic field; in contrast, large filaments, which all have higher column densities, are oriented nearly perpendicular (or, in the case of G202, more nearly parallel) to the magnetic field. In the two nearby fields (G210 and G300), we observe a transition from mostly parallel to mostly perpendicular relative orientations at an H 2 column density ≃ 1.1 × 10 21 cm -2 and 1.4 × 10 21 cm -2 , respectively, consistent with the results of previous studies. Conclusions. Our results confirm the existence of a coupling between magnetic fields at cloud scales and filaments at smaller scale. They also illustrate the potential of combining Herschel and Planck observations, and they call for further statistical analyses with our dedicated method.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1990

Computation and parallel implementation for early vision

The problem of early vision is to transform one or more retinal illuminance images-pixel arrays-to image representations built out of such primitive visual features such as edges, regions, disparities, and clusters. These transformed representations form the input to later vision stages that perform higher level vision tasks including matching and recognition. Researchers developed algorithms for: (1) edge finding in the scale space formulation; (2) correlation methods for computing matches between pairs of images; and (3) clustering of data by neural networks. These algorithms are formulated for parallel implementation of SIMD machines, such as the Massively Parallel Processor, a 128 x 128 array processor with 1024 bits of local memory per processor. For some cases, researchers can show speedups of three orders of magnitude over serial implementations.

Gualtieri, J. Anthony↗
NASA NTRS1999

MLP: A Parallel Programming Alternative to MPI for New Shared Memory Parallel Systems

Recent developments at the NASA AMES Research Center's NAS Division have demonstrated that the new generation of NUMA based Symmetric Multi-Processing systems (SMPs), such as the Silicon Graphics Origin 2000, can successfully execute legacy vector oriented CFD production codes at sustained rates far exceeding processing rates possible on dedicated 16 CPU Cray C90 systems. This high level of performance is achieved via shared memory based Multi-Level Parallelism (MLP). This programming approach, developed at NAS and outlined below, is distinct from the message passing paradigm of MPI. It offers parallelism at both the fine and coarse grained level, with communication latencies that are approximately 50-100 times lower than typical MPI implementations on the same platform. Such latency reductions offer the promise of performance scaling to very large CPU counts. The method draws on, but is also distinct from, the newly defined OpenMP specification, which uses compiler directives to support a limited subset of multi-level parallel operations. The NAS MLP method is general, and applicable to a large class of NASA CFD codes.

Taft, James R.↗
DOE OSTI2020

Spatiotemporal parallelization of an analytical heat conduction model for additive manufacturing via a hybrid OpenMP + MPI approach

The ability to do thermal simulations for entire additive manufacturing builds is a key computational problem facing the additive manufacturing community; however, complex numerical models considering multiple physical phenomena currently do not have the capacity for simulations at this scale. To this end, conduction only analytic models offer a viable approach due to the massive drop in computational expense. In this work, we extend an existing implementation which uses a governing equation which can be evaluated at any point in space and time. This implementation already utilizes OpenMP with a spatial decompositions scheme stemming from a melt pool tracking algorithm. Furthermore, we then combine this with a parallel in time (PinT) approach to make the problem highly parallelizable. The new scheme, which uses MPI for internode communication and OpenMP for intranode communication, is shown to scale very well across multiple computational nodes. This approach results in the ability to simulate the 3D solidification conditions for entire layers of additively manufactured parts in minutes making part scale thermal simulations more practical.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area

This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Methane Production by Co-Digesting Vinasse and Whey in an AnSBBR: Effect of Mixture Ratio and Feed Strategy

The most common approach to deal with vinasse (sugarcane stillage) is fertigation, but this technique compromises soil structure and surrounding water bodies. A possible solution is to transport vinasse to local cheese whey producers and perform the co-digestion of these wastewaters together, reducing their organic load and generating bioenergy. Therefore, this study investigated the application of an AnSBBR (anaerobic sequencing batch biofilm reactor) operated in batch and fed-batch mode, co-digesting vinasse and whey at 30 °C. The effect of influent composition and feeding strategy was assessed. In all conditions, the system achieved high organic matter removal (approximately 83%). Increasing the percentage of vinasse from 0 to 100% in the influent resulted in a decrease in methane productivity (76.3 to 51.1 molCH{sub 4} m{sup −3} day{sup −1}) and yield (12.7 to 9.1 molCH{sub 4} kgCOD{sup −1}), but fed-batch mode operation improved reactor performance (73.0 molCH{sub 4} m{sup −3} day{sup −1} and 11.5 molCH{sub 4} kgCOD{sup −1}). From the kinetic metabolic model, it was possible to infer that, at the best condition, methane is produced in a similar way from the acetoclastic and hydrogenotrophic routes. A scheme of four parallel reactors with a volume of 16,950 m{sup 3} each was proposed in the scale-up estimation, with an energy recovery estimated in 28,745 MWh per month.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks

Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

Science & Technology - Other Topics↗