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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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27 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a highly optimized implementation that scales to {\herogpucount} NVIDIA Volta GPUs. We develop a hierarchical scheme based on a multi-player game-theoretic approach for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. Our implementation scales to {\heronodecount} nodes on the Summit supercomputer with a \weakscalingeff\% scaling efficiency, achieving peak and sustained half-precision rates of {\heropeakperf} PF/s and {\herosustainedperf} PF/s.

physics informed ML, machine learning, generative ↗
DOE OSTI2020

Fission Batteries: Definition, Key Attributes and Research and Development Needs

The term battery was first introduced by the American scientist and inventor Benjamin Franklin in 1749, followed by invention of the first true battery in 1800 by Alessandro Volta. Today, batteries are a ubiquitous source of portable electric power across different applications. Batteries are widely used across a range of scales from consumer products to grid-scale energy storage. There are different types of batteries, but they can be broadly classified as chemical or electric batteries [1], atomic batteries, nuclear batteries, tritium batteries or radioisotope generators [2, 3]. Atomic batteries, nuclear batteries, tritium batteries, and radioisotope generators have gained significant attention for applications requi-ing long-term power supply and high-power density, including the space power reactor [4]. While a number of reactor systems, particularly micro-reactors, are referred to as fission batteries, this paper defines key attributes required for a fission reactor to achieve capabilities comparable with the characteristics of batteries that enable their wide-spread use. Achieving this full vision of a fission battery will enable broad deployment of fission reactors that function like batteries. The research and development (R&D) activities needed to achieve the desired fission battery attributes are discussed below.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

GPU-based optical simulation of the DARWIN detector

Abstract Understanding propagation of scintillation light is critical for maximizing the discovery potential of next-generation liquid xenon detectors that use dual-phase time projection chamber technology. This work describes a detailed optical simulation of the DARWIN detector implemented using Chroma, a GPU-based photon tracking framework. To evaluate the framework and to explore ways of maximizing efficiency and minimizing the time of light collection, we simulate several variations of the conventional detector design. Results of these selected studies are presented. More generally, we conclude that the approach used in this work allows one to investigate alternative designs faster and in more detail than using conventional Geant4 optical simulations, making it an attractive tool to guide the development of the ultimate liquid xenon observatory.

Instruments & Instrumentation↗
DOE OSTI2020

Chemical Sensing and Chemoresponsive Pumping with Conical-Pore Polymeric Membranes

Synthetic membranes containing asymmetrically shaped pores have been shown to rectify the ionic current flowing through the membrane. Ion-current rectification means that such membranes produce nonlinear current–voltage curves analogous to those observed with solid-state diode rectifiers. In order to observe this ion-current rectification phenomenon, the asymmetrically shaped pores must have pore-wall surface charge. Pore-wall surface charge also allows for electroosmotic flow (EOF) to occur through the membrane. We have shown that, because ion-current is rectified, EOF is likewise rectified in such membranes. This means that flow through the membrane depends on the polarity of the voltage applied across the membrane, one polarity producing a higher, and the opposite producing a lower, flow rate. As is reviewed here, these ion-current and EOF rectification phenomena are being used to develop new sensing technologies. Results obtained from an ion-current-based sensor for hydrophobic cations are reviewed. In addition, ion-current and EOF rectification can be combined to make a new type of device—a chemoresponsive nanofluidic pump. This is a pump that either turns flow on or turns flow off, when a specific chemical species is detected. Results from a prototype Pb 2+ chemoresponsive pump are also reviewed here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Potentiometric Studies on Ion-Transport Selectivity in Charged Gold Nanotubes

Under ideal conditions, nanotubes with a fixed negative tube-wall charge will reject anions and transport-only cations. Because many proposed nanofluidic devices are optimized in this ideally cation-permselective state, it is important to know the experimental conditions that produce ideal responses. A parameter called Ccrit, the highest salt concentration in a contacting solution that still produces ideal cation permselectivity, is of particular importance. Pioneering potentiometric studies on gold nanotubes were interpreted using an electrostatic model that states that Ccrit should occur when the Debye length in the contacting salt solution becomes equivalent to the tube radius. Since this “double-layer overlap model” (DLOM), treats all same-charge ions as identical point charges, it predicts that all same-charged cations should produce the same Ccrit. However, the effect of cation on Ccrit in gold nanotubes was never investigated. This knowledge gap has become important because recent studies with a polymeric cation-permselective nanopore membrane showed that DLOM failed for every cation studied. To resolve this issue, we conducted potentiometric studies on the effect of salt cation on Ccrit for a 10 nm diameter gold nanotube membrane. Ccrit for all cations studied were, within experimental error, the same and identical, with values predicted by DLOM. The reason DLOM prevailed for the gold nanotubes but failed for the polymeric nanopores stems from the chemical difference between the fixed negative charges of these two membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗