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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ion and Electron Acceleration in Fully Kinetic Plasma Turbulence

Turbulence is often invoked to explain the origin of nonthermal particles in space and astrophysical plasmas. By means of 3D fully kinetic particle-in-cell simulations, we demonstrate that turbulence in low-β plasmas (β is the ratio of plasma pressure to magnetic pressure) accelerates ions and electrons into a nonthermal energy distribution with a power-law energy range. The ion spectrum is harder than the electron one, and both distributions get steeper for higher β. We show that the energization of electrons is accompanied by a significant energy-dependent pitch-angle anisotropy, with most electrons moving parallel to the local magnetic field, while ions stay roughly isotropic. We demonstrate that particle injection from the thermal pool occurs in regions of high current density. Parallel electric fields associated with magnetic reconnection are responsible for the initial energy gain of electrons, whereas perpendicular electric fields control the overall energization of ions. Our findings have important implications for the origin of nonthermal particles in space and astrophysical plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An Observed Transition of Galaxy Spins on Void Surfaces

In linear theory, the galaxy angular momentum vectors that originate from initial tidal interactions with surrounding matter distribution intrinsically develop perpendicular alignments with the directions of maximum matter compression, regardless of galaxy mass. In simulations, however, galaxy spins exhibit parallel alignments in the mass range lower than a certain threshold, which depends on redshift, web type, and background cosmology. We show that the observed three-dimensional spins of the spiral galaxies located on the void surfaces from the Sloan Digital Sky Survey indeed transit from perpendicular to parallel alignments with the directions toward the nearest void centers at the threshold zone, 9.51 $\leqslant$ log[M th,$\star$ /(h -1 M ⊙ )] $\leqslant$ 10.03. This study presents the first direct observational evidence for the occurrence of mass-dependent spin transition of real galaxies with respect to non-filamentary structures of the cosmic web, opening a way to constrain the initial conditions of the early universe by measuring the spin transition threshold.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Propulsion Electrification Architecture Selection Process and Cost of Carbon Abatement Analysis for Heavy-Duty Off-Road Material Handler

The heavy-duty off-road industry continues to expand efforts to reduce fuel consumption and CO 2 e (carbon dioxide equivalent) emissions. Many manufacturers are pursuing electrification to decrease fuel consumption and emissions. Future policies will likely require electrification for CO2e savings, as seen in light-duty on-road vehicles. Electrified architectures vary widely in the heavy-duty off-road space, with parallel hybrids in some applications and series hybrids in others. The diverse applications for different types of equipment mean different electrified configurations are required. Companies must also determine the value in pursuing electrified architectures; this work analyzes a range of electrified architectures, from micro hybrids to parallel hybrids to series hybrids to a BEV, looking at the total cost, total CO 2 e, and cost per CO 2 e (cost of carbon abatement, or cost of carbon reduction) using data for the year 2021. This study is focused on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i. This machine’s specialty application, including events like unloading large oil pipes from a railcar, requires a unique electrified architecture that suits its specific needs. However, the results from this study may be extrapolated to similar machinery to inform fuel savings options across the heavy-duty off-road industry. In this study, a unique electrified architecture is determined for the Cary-Lift. This architecture is informed by multiple rounds of a Pugh matrix decision analysis to select a shortened list of desirable electrified architectures. The shortened list is modeled and simulated to determine CO 2 e, cost, and cost per CO 2 e. A final architecture is determined as a plug-in series hybrid that reduces fuel consumption by 65%, targeting the large fuel and CO 2 e savings that are likely to be required for the future of the heavy-duty off-road industry.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Self-potential tomography preconditioned by particle swarm optimization—Self-potential monitoring and streamflow data acquired March 26–September 14, 2023 at East Fork Poplar Creek near Oak Ridge Tennessee

This data release contains self-potential (SP) monitoring data measured on the flood plain of East Fork Poplar Creek (East Fork) in Oak Ridge, Tennessee and streamflow data measured at streamgage EFK5.4 about 310 meters upstream from the SP monitoring site. Additionally, forward and inverse numerical modeling scripts used to model the electrical-potential field on the East Fork flood plain are provided. SP monitoring data included in this data release were measured at 39 different data-collection points on the east flood plain; 30 points were spaced 3-m apart along an 87-m profile parallel to the edge of the streambank, and 9 points were spaced 5-m apart along a 40-m profile approximately perpendicular to the streambank. The two profiles of SP data-collection points intersected at the approximate midpoint of the profile parallel to the streambank. Transient voltages were measured at each data-collection point every 60 seconds between 16:13 Eastern Standard Time (EST) on March 26, 2023, and 11:41 EST on September 14, 2023. Streamflow data included in this data release overlap the time-period of self-potential monitoring and were measured every 900 seconds between 16:23 on March 26, 2023, and 23:53 on September 14, 2023.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)

Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but much more difficult to write. Julia is a programming language that aims to be the best of both worlds – a development and production language at the same time. To test Julia's utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, the Model for Prediction Across Scales–Ocean (MPAS-Ocean), as well as a Python shallow water code. Three versions of the Julia shallow water code were created: for single-core CPU, graphics processing unit (GPU), and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the Julia model was 13 times faster than Python using NumPy, where both used an unthreaded single-core CPU. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20× speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code was almost identical in terms of performance to the MPI parallelized code on 64 processes, an unexpected result for such different architectures. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts and ranges from 2× faster to 2× slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling commercial-scale CO 2 storage in the gas hydrate stability zone with PFLOTRAN v6.0

Abstract. Safe and secure carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. In the last decade, there has been a significant increase in activity associated with reservoir characterization and site selection for large-scale CO2 storage projects across the globe. These prospective storage sites tend to be selected for their optimal structural, petrophysical, and geochemical trapping potential. However, it has also been suggested that storing CO2 in reservoirs within the CO2 hydrate stability zone (GHSZ), characterized by high pressures and low temperatures (e.g., Arctic or marine environments), could provide a natural thermodynamic barrier to gas leakage. Evaluating the prospect of commercial-scale, long-term storage of CO2 in the GHSZ requires reservoir-scale modeling capabilities designed to account for the unique physics and thermodynamics associated with these systems. We have developed the HYDRATE flow mode and the accompanying fully implicit parallel well model in the massively parallel subsurface flow and reactive transport simulator PFLOTRAN to model CO2 injection into the marine GHSZ. We have applied these capabilities to a set of CO2 injection scenarios designed to reveal the challenges and opportunities for commercial-scale CO2 storage in the GHSZ.

carbon storage↗
DOE OSTI2024

Grand challenges of wind energy science – meeting the needs and services of the power system

The share of wind power in power systems is increasing dramatically, and this is happening in parallel with increased penetration of solar photovoltaics, storage, other inverter-based technologies, and electrification of other sectors. Recognising the fundamental objective of power systems, maintaining supply–demand balance reliably at the lowest cost, and integrating all these technologies are significant research challenges that are driving radical changes to planning and operations of power systems globally. In this changing environment, wind power can maximise its long-term value to the power system by balancing the needs it imposes on the power system with its contribution to addressing these needs with services. A needs and services paradigm is adopted here to highlight these research challenges, which should also be guided by a balanced approach, concentrating on its advantages over competitors. The research challenges within the wind technology itself are many and varied, with control and coordination internally being a focal point in parallel with a strong recommendation for a holistic approach targeted at where wind has an advantage over its competitors and in coordination with research into other technologies such as storage, power electronics, and power systems.

17 WIND ENERGY↗
DOE OSTI2026

rustpix

rustpix is a high-performance, open-source Rust library with first-class Python bindings (via PyO3) for processing pixel-detector data in neutron imaging. It targets time-stamping detectors such as Timepix3 (TPX3) at ORNL's Spallation Neutron Source (VENUS beamline), where each detected neutron deposits charge across a cluster of pixels within a very high-rate event stream (96M+ hits/sec). rustpix parses TPX3 event data in parallel using memory-mapped I/O, offers four interchangeable clustering algorithms (ABS adjacency-based search, DBSCAN, graph/union-find connected components, and a parallel grid method), and extracts weighted, super-resolved centroids to produce neutron-event lists. A streaming architecture lets it process files larger than available memory. rustpix is distributed as a pip-installable Python package (with NumPy integration), Rust crates, a command-line tool, and an interactive GUI; it writes HDF5, Apache Arrow, and CSV; and it is designed to extend to TPX4 and other detector types. Released as open-source under the MIT License.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

Testing of a 40-kWth Counterflow Particle-Supercritical Carbon Dioxide Narrow-Channel, Fluidized Bed Heat Exchanger

Particle-based primary heat exchangers (HXs) must deliver sCO2 fluid temperatures above 700°C to couple particle-based concentrating solar receivers and thermal energy storage (TES) sub-systems with efficient sCO2 power cycles. Particle-sCO2 HX designs have struggled to meet DOE cost targets (≤ $150/kWth) due to the amount of expensive nickel alloys necessary for manufacturing full-scale, particle-sCO2 HXs. Our team has demonstrated that mild bubbling fluidization of falling particles in a counterflow narrow-channel fluidized bed can reduce required HX surface area and thus, costs by increasing particle-wall heat transfer coefficients hT,w > 800 W m-2 K-1. This paper reports on the fabrication and testing of a stainless steel, particle-sCO2 HX with 12 fluidized-bed channels approximately 10.5 mm deep spaced between diffusion-bonded, micro-channel sCO2 plates. The HX with a core length of ≈0.56 m is fed with CARBOBEAD HSP particles through a short, fluidized freeboard zone just above the core. Testing to date in the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories has shown that parallel bed fluidization maintains uniform particle inventory across the instrumented channels. Heat transfer thermal duty between the particle and sCO2 flows exceeds 30 kWth with sCO2 inlet temperatures of 200ºC and particle inlet temperatures up to 440ºC and mass flow rates of 0.2 kg s-1 fluidized by counterflowing gas flow rates of 0.005 kg s-1. Tests at higher particle and sCO2 inlet temperatures (600ºC and 400ºC respectively) are targeted to achieve > 40 kWth with model-predicted overall heat transfer coefficients U > 400 W m-2 K-1.

14 SOLAR ENERGY↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2020

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.

Zhang, Xingyao↗
DOE OSTI2020

Method of making a distributed optical fiber sensor having enhanced Rayleigh scattering and enhanced temperature stability, and monitoring systems employing same

A method of making an optical fiber sensor device for distributed sensing includes generating a laser beam comprising a plurality of ultrafast pulses, and focusing the laser beam into a core of an optical fiber to form a nanograting structure within the core, wherein the nanograting structure includes a plurality of spaced nanograting elements each extending substantially parallel to a longitudinal axis of optical fiber. Also, an optical fiber sensor device for distributed sensing includes an optical fiber having a longitudinal axis, a core, and a nanograting structure within the core, wherein the nanograting structure includes a plurality of spaced nanograting elements each extending substantially parallel to the longitudinal axis of the optical fiber. Also, a distributed sensing method and system and an energy production system that employs such an optical fiber sensor device.

42 ENGINEERING↗
DOE OSTI2020

Credit based flow control mechanism for use in multiple link width interconnect systems

Flow control credit management is provided when converting traffic from a first parallel link width on a first link to a second parallel link width on a second link A current value is calculated for a variable flow control credit exchange rate (R) associated with the first and second links. A first flow control credit indicator is received on the second link, and a credit amount calculated based on the first flow control credit indicator and R. A second flow control credit indicator for the credit amount is then transmitted on the first link.

42 ENGINEERING↗
DOE OSTI2020

Component and efficient plate and frame absorber

An absorber for an absorption refrigeration system for a vertically mounted plate absorber has fins positioned over the plate to promote distribution of a fluid under flow as a nearly continuous thin film. The absorber has rows of fins that are evenly spaced with the fins occupying a portion of the active width of the plate with adjacent rows offset by the width of the plate. A combined evaporator-absorber module is constructed with the absorbing face of the absorber parallel with an evaporating surface of an evaporator where absorber solution and refrigerant fall as parallel falling sheets of liquid. The absorber is covered with a porous hydrophobic membrane to isolate the falling absorber solution from the falling liquid refrigerant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Embedding global barrier and collective in a torus network

Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computer program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adaptive multi-level checkpointing

In some examples, with respect to adaptive multi-level checkpointing, a transfer parameter associated with transfer of checkpoint data from a node-local storage to a parallel file system may be ascertained for the checkpoint data stored in the node-local storage. The transfer parameter may be compared to a specified transfer parameter threshold. A determination may be made, based on the comparison of the transfer parameter to the specified transfer parameter threshold, as to whether to transfer the checkpoint data from the node-local storage to the parallel file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Peptides having reduced toxicity that stimulate cholesterol efflux

The present invention provides a family of non-naturally occurring polypeptides having cholesterol efflux activity that parallels that of full-length apolipoproteins (e.g., Apo AI and Apo E), and having high selectivity for ABCA1 that parallels that of full-length apolipoproteins. Further, the peptides of the invention have little or no toxicity when administered at therapeutic and higher doses. The invention also provides compositions comprising such polypeptides, methods of identifying, screening and synthesizing such polypeptides, and methods of treating, preventing or diagnosing diseases and disorders associated with dyslipidemia, hypercholesterolemia, or inflammation; or diseases involving abnormal glucose metabolism, e.g., diabetes, metabolic syndrome; or Alzheimer's Disease or frontotemporal dementia.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks

Parallel training of an ensemble of Deep Neural Networks (DNN) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task. Prior efforts have shown that data sharing, where the common preprocessing operation is shared across the DNN training pipelines, saves computational resources and improves pipeline efficiency. Data sharing strategy, however, performs poorly for a heterogeneous set of DNNs where each DNN has varying computational needs and thus different training rate and convergence speed. This paper proposes FLEET, a flexible ensemble DNN training framework for efficiently training a heterogeneous set of DNNs. We build FLEET via several technical innovations. We theoretically prove that an optimal resource allocation is NP-hard and propose a greedy algorithm to efficiently allocate resources for training each DNN with data sharing. We integrate data-parallel DNN training into ensemble training to mitigate the differences in training rates and introduce checkpointing into this context to address the issue of different convergence speeds. Experiments show that FLEET significantly improves the training efficiency of DNN ensembles without compromising the quality of the result.

Guan, Hui↗