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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advantages of Superconducting Device using 26 MgB2 Wire

Superconducting undulator (SCU) which can run at higher temperature than 4 Kelvin range has been anticipated for a long time. However, the performance of high-temperature superconductor (HTS) tapes have not improved over the last decade as the industry mostly concentrates on the cost reduction. Magnesium Diboride (MgB2) has a critical temperature of 39 K and the 1 st generation (1G) wire has been in production but it was not sufficient to be used in SCU. Recently the 2 nd generation (2G) wire has been developed. It is estimated to give a similar performance as NbTi wire at 20K. In this paper the advantages of a SCU with 2G MgB2 wire compared to a conventional SCU with NbTi or Nb3Sn3 wire.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Using Computational Storage Devices: OpenMP/MPI and Charliecloud [Slides]

Originally used Spark and HadoopFS. Collected interesting results, but this method had its issues: limited application, too much overhead to gauge CSDs’ raw performance. Solution? Rewrite our benchmarks without Spark using Serial Python and Serial & Parallel C++ (Combinations of OpenMP & OpenMPI). Compared to Spark and Python, C++ implementation is a lot faster. Caveat: an expert with Spark or Python would likely be able to improve the performance of those implementations. Computational power of our CSDs seem to be much lower than the host machine. Using all 4 cores of a single CSD, the job takes ~6.8x longer than using just one core on the host machine. Host also seems to scale better with increasing file size. Resulting Question: When, if ever, would it make sense to use CSDs for compute rather than a much-faster host?

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Offloading Calculations to Computational Storage Devices: Spark and HDFS [Slides]

The objective is to evaluate the capabilities of multiple CSDs (provided by NDG Systems) using Hadoop Filesystem and Apache Spark. The independent variables are: number of CSDs, 0, 1, 2, 4, or 6; size of dataset, 1 GB, 5 GB, 10 GB; type of dataset, one large file with all of the data, 10 files, 100 files. The dependent variables are: job time; execution time. the constants are operations on the dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Photovoltaics Research and Development: Device Architecture for Next-Generation CdTe PV. Cooperative Research and Development Final Report, CRADA Number CRD-17-00662

Despite remarkable efficiency improvements at the cell and module level, thin-film CdTe PV suffers from a significant voltage deficit compared with the ideal. The proposed solution is to build cells with fully depleted absorbers and with energy barriers front and back to direct electrons and holes in appropriate directions. Combined with minimal interfacial recombination, this approach, which is similar to the successful silicon HIT cells, circumvents the need for higher lifetimes and carrier concentration to achieve higher voltage, Colorado State is particularly well-positioned to the address the depleted absorbed architecture. It has robust single-vacuum closed-spaced-sublimation deposition chamber, currently being enhanced with new sources for CdTe alloys and grading. It has made reasonable-efficiency cells with absorbers as thin as 0.3 um, it has demonstrated electron reflection at the rear of the absorber with a continuous CdMgTe layer, it has replaced CdS with MgZnO, which has several advantages including tunable band offsets, and it has achieved 16.8% efficiency. It also has a mature suite of measurement equipment for cell evaluation and the experience to effectively analyze its results for refinement in cell fabrication. Several healthy collaborations will continue within the CdTe community.

14 SOLAR ENERGY↗
DOE OSTI2021

Mapping Stochastic Devices to Probabilistic Algorithms

Probabilistic and Bayesian neural networks have long been proposed as a method to incorporate uncertainty about the world (both in training data and operation) into artificial intelligence applications. One approach to making a neural network probabilistic is to leverage a Monte Carlo sampling approach that samples a trained network while incorporating noise. Such sampling approaches for neural networks have not been extensively studied due to the prohibitive requirement of many computationally expensive samples. While the development of future microelectronics platforms that make this sampling more efficient is an attractive option, it has not been immediately clear how to sample a neural network and what the quality of random number generation should be. This research aimed to start addressing these two fundamental questions by examining basic “off the shelf” neural networks can be sampled through a few different mechanisms (including synapse “dropout” and neuron “dropout”) and examine how these sampling approaches can be evaluated both in terms of evaluating algorithm effectiveness and the required quality of random numbers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Develop On-Demand nanoplasmonic Device Concepts in a Semiconductor Compatible Hybrid System. Final Report

This report summarizes the research activities supported by the U.S. Department of Energy grant DE-SC-0010399. The goal of this project is to is to develop an on-demand nanoplasmonic platform that combines emerging oxide nanoelectronics with a low-loss semiconductor-friendly plasmonic materials. Toward this goal, several unique heterostructures formed between complex oxides and 2D layered materials have been developed. Based on these material systems, a set of atomic force microscope (AFM) based techniques have been developed that can allow long-living and reversible material phase transitions to be manipulated in nanoscale at room temperature. Using such technique and through interface coupling, we can effectively tune the plasmon propagations in unconventional plasmonic materials such as graphene and VO 2 . In addition, we also discovered a plethora of novel interface enabled electro-optic effects, including the spatial modulation of light using 2DEG, a giant programmable photothermoelectric effects at room temperature, a light induced superconducting transition, and a tunable giant third order optical nonlinearities. These major results are outlined in this report, along with lists of project participants and products.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Radiation-Generating Device Safety Self-Study [Slides]

Self-study slides are included for the following modules: RGD Safety Course Presentation, Radiation Detection, Biological Effects, RGDs, Radiation Protection Principles, Production of X-Rays, Protective Measures, and Responsibilities for X-Ray Safety.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Heterostructures of Various Dimensional Materials—From Epitaxy to Device Applications

The instructor will discuss a new epitaxy lift-off technique, a so-called remote epitaxy that can produce freestanding semiconductor membranes. Discussion will include 1) Remote epitaxy mechanism 2) High yield peeling mechanism 3) Reusability of the substrates 4) Economic aspect of remote epitaxy and 5) Heterointegration of 3D materials with 2D materials for advanced heterostructuring.

Kim, Jeehwan↗
DOE OSTI2022

Distances to Prompt Effects for a Nuclear Device

In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗