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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗
DOE OSTI2014

Global Simulation of Bioenergy Crop Productivity: Analytical framework and Case Study for Switchgrass

Contemporary global assessments of the deployment potential and sustainability aspects of biofuel crops lack quantitative details. This paper describes an analytical framework capable of meeting the challenges associated with global scale agro-ecosystem modeling. We designed a modeling platform for bioenergy crops, consisting of five major components: (i) standardized global natural resources and management data sets, (ii) global simulation unit and management scenarios, (iii) model calibration and validation, (iv) high-performance computing (HPC) modeling, and (v) simulation output processing and analysis. A case study with the HPC- Environmental Policy Integrated Climate model (HPC-EPIC) to simulate a perennial bioenergy crop, switchgrass (Panicum virgatum L.) and global biomass feedstock analysis on grassland demonstrates the application of this platform. The results illustrate biomass feedstock variability of switchgrass and provide insights on how the modeling platform can be expanded to better assess sustainable production criteria and other biomass crops. Feedstock potentials on global grasslands and within different countries are also shown. Future efforts involve developing databases of productivity, implementing global simulations for other bioenergy crops (e.g. miscanthus, energycane and agave), and assessing environmental impacts under various management regimes. We anticipated this platform will provide an exemplary tool and assessment data for international communities to conduct global analysis of biofuel biomass feedstocks and sustainability.

Nair, S. Surendran↗
DOE OSTI2014

Direct mounted photovoltaic device with improved adhesion and method thereof

The present invention is premised upon a photovoltaic device suitable for directly mounting on a structure. The device includes an active portion including a photovoltaic cell assembly having a top surface portion that allows transmission of light energy to a photoactive portion of the photovoltaic device for conversion into electrical energy and a bottom surface having a bottom bonding zone; and an inactive portion immediately adjacent to and connected to the active portion, the inactive portion having a region for receiving a fastener to connect the device to the structure and having on a top surface, a top bonding zone; wherein one of the top and bottom bonding zones comprises a first bonding element and the other comprises a second bonding element, the second bonding element designed to interact with the first bonding element on a vertically overlapped adjacent photovoltaic device to bond the device to such adjacent device or to the structure.

14 SOLAR ENERGY↗
DOE OSTI2015

YBCO Coated Conductors

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Paranthaman, Mariappan Parans↗
DOE OSTI2016

Column Testing and 1D Reactive Transport Modeling to Evaluate Uranium Plume Persistence Processes

Motivation for Study: Natural flushing of contaminants at various U.S. Department of Energy Office of Legacy Management sites is not proceeding as quickly as predicted (plume persistence) Objectives: Help determine natural flushing rates using column tests. Use 1D reactive transport modeling to better understand the major processes that are creating plume persistence Approach: Core samples from under a former mill tailings area Tailings have been removed. Column leaching using lab-prepared water similar to nearby Gunnison River water. 1D reactive transport modeling to evaluate processes

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Sandia OpenSHMEM

Sandia OpenSHMEM is an implementation of the OpenSHMEM Standard over the Portals 4 Networking API and the OpenFabrics Interface (OFI). Sandia OpenSHMEM is designed to be a low-overhead implementation of the OpenSHMEM standard which takes advantage of the many features of the Portals 4 specification.

Barrett, Brian W.↗