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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Management Tools

Manugistics, Inc. (formerly AVYX, Inc.) has introduced a new programming language for IBM and IBM compatible computers called TREES-pls. It is a resource management tool originating from the space shuttle, that can be used in such applications as scheduling, resource allocation project control, information management, and artificial intelligence. Manugistics, Inc. was looking for a flexible tool that can be applied to many problems with minimal adaptation. Among the non-government markets are aerospace, other manufacturing, transportation, health care, food and beverage and professional services.

Source record↗
NASA NTRS1999

Analysis of Carbon Nanotube Field-Effect-Transistors (FETs)

This five page presentation is grouped into 11 numbered viewgraphs, most of which contain one or more diagrams. Some of the diagrams are accompanied by captions, including: 2) Nanotube FET by Delft, IBM; 3) Nanotube FET/Standard MOSFET; 5) Saturation with carrier-carrier; 7) Electronic properties of carbon nanotube; 8) Theoretical nanotube FET characteristics; 11) Summary: Delft and IBM nanotube FET analysis.

Yamada, Toshishige↗
NASA NTRS1994

Parallel Calculation of Sensitivity Derivatives for Aircraft Design using Automatic Differentiation

Sensitivity derivative (SD) calculation via automatic differentiation (AD) typical of that required for the aerodynamic design of a transport-type aircraft is considered. Two ways of computing SD via code generated by the ADIFOR automatic differentiation tool are compared for efficiency and applicability to problems involving large numbers of design variables. A vector implementation on a Cray Y-MP computer is compared with a coarse-grained parallel implementation on an IBM SP1 computer, employing a Fortran M wrapper. The SD are computed for a swept transport wing in turbulent, transonic flow; the number of geometric design variables varies from 1 to 60 with coupling between a wing grid generation program and a state-of-the-art, 3-D computational fluid dynamics program, both augmented for derivative computation via AD. For a small number of design variables, the Cray Y-MP implementation is much faster. As the number of design variables grows, however, the IBM SP1 becomes an attractive alternative in terms of compute speed, job turnaround time, and total memory available for solutions with large numbers of design variables. The coarse-grained parallel implementation also can be moved easily to a network of workstations.

Bischof, c. H.↗
NASA NTRS1992

Laboratory Reflectance Spectra of 160 Minerals, 0.4 to 2.5 Micrometers

This reports provides reflectance spectra of 160 minerals in both digital and printed form. In order to demonstrate the effect of particle size on reflectance, the spectral data for 135 of the minerals are presented at three different grain sizes. These are 125-500 microns, 45-125 microns and less than 45 microns. Ancillary information is provided with each mineral spectrum, which includes the mineral name, minerology, supplier, sampling locality and our designated sample number. The purity of every mineral sample was evaluated by X-ray diffraction (XRD). The composition of certain minerals known to deviate strongly from idealized end-member compositions was determined by electron microprobe analysis. The compositional information obtained by microbe analysis and accessory minerals identified by XRD are noted with the ancillary information. In addition, the spectrum acquired from the coarsest grain size available for each sample was processed with a feature-finding algorithm to quantify the characteristics of the spectral absorption features. All the reflectance spectra presented here are provided in digital form on IBM-compatible 3.5" diskettes. Also included on diskette is a program for displaying the spectral data and searching for spectral features that runs on an IBM-compatible PC with standard VCA graphics.

reflectance spectra↗
NASA NTRS2001

Thermal Analysis of Compressible CO2 Flow for Major Equipment of Fire Detection System

A thermal analysis of the compressible CO2 flow for the Portable Fire Extinguisher (PFE) system has been performed. The purpose of this analysis is to determine the discharged CO2 mass from the PFE tank through the Temporary Sleep Station (TeSS) nozzle in reflecting to the latest design of the extended nozzle, and to evaluate the thermal issues associated to the latest nozzle configuration. A SINDA/FLUINT model has been developed for this analysis. The model includes the PFE tank and the TeSS nozzle, and both have initial temperature of 72 of. In order to investigate the thermal effect on the nozzle due to discharging C02, the PFE TeSS nozzle pipe has been divided into three segments. This model also includes heat transfer predictions for PFE tank inner and outer wall surfaces. The simulation results show that the CO2 discharge rates have fulfilled the minimum flow requirements that the PFE system discharges 3.0 Ibm CO2 in 10 seconds and 5.5 Ibm of CO2 in 45 seconds during its operation. At 45 seconds, the PFE tank wall temperature is 63 OF, and the TeSS nozzle cover wall temperatures for the three segments are 47 OF, 53 OF and 37 OF, respectively. Thermal insulation for personal protection is used for the first two segments of the TeSS nozzle. The simulation results also indicate that at 50 seconds, the remaining CO2 in the tank may be near the triple point (gas, liquid and solid) state and, therefore, restricts the flow.

Zhang, Michael Y.↗
NASA NTRS2002

Thermal Analysis of Compressible CO2 Flow for PFE TeSS Nozzle of Fire Detection System

A thermal analysis of the compressible CO2 flow for the Portable Fire Extinguisher (PFE) system has been performed. A SINDA/FLUINT model has been developed for this analysis. The model includes the PFE tank and the TeSS nozzle, and both have initial temperature of 72 F. In order to investigate the thermal effect on the nozzle due to discharging CO2, the PFE TeSS nozzle pipe has been divided into three segments. This model also includes heat transfer predictions for PFE tank inner and outer wall surfaces. The simulation results show that the CO2 discharge rates have fulfilled the minimum flow requirements that the PFE system discharges 3.0 Ibm CO2 in 10 seconds and 5.5 Ibm of CO2 in 45 seconds during its operation. At 45 seconds, the PFE tank wall temperature is 63 F, and the TeSS nozzle cover wall temperatures for the three segments are 47 F, 53 F and 37 F, respectively. Thermal insulation for personal protection is used for the first two segments of the TeSS nozzle. The simulation results also indicate that at 50 seconds, the remaining CO2 in the tank may be near the triple point (gas, liquid and solid) state and, therefore, restricts the flow.

Zhang, Y. Michael↗
NASA NTRS2014

Advanced CMOS Radiation Effects Testing and Analysis

Presentation at the annual NASA Electronic Parts and Packaging (NEPP) Program Electronic Technology Workshop (ETW). The material includes an update of progress in this NEPP task area over the past year, which includes testing, evaluation, and analysis of radiation effects data on the IBM 32 nm silicon-on-insulator (SOI) complementary metal oxide semiconductor (CMOS) process. The testing was conducted using test vehicles supplied by directly by IBM.

cmos↗
NASA NTRS2016

A Quantum Annealing Computer Team Addresses Climate Change Predictability

The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.

Science Data Processing↗
NASA NTRS2018

Using Knowledge Analytics to Search and Characterize Mass Properties Aerospace Data

There is growing capability in the field of “Big Data” and “Data Analytics” which Mass Properties Engineers might like to take advantage of. This paper utilizes an implementation of the IBM Knowledge Analytics and Watson search capabilities to explore a corpus of material developed primarily with the interests of Mass Properties Engineers and vehicle concept developers at its forefront. The full collection of SAWE (Society of Allied Weight Engineers, Inc.) Technical Papers from 1939 through 2015 is a major portion of the knowledge content. Additional aerospace vehicle design information includes metadata from AIAA (American Institute for Aeronautics and Astronautics), and INCOSE (International Council on Systems Engineering) as well as author-provided personal search material. This data is processed with respect to certain expected content, data taxonomies and key words to become the core data in NASA Langley Research Center’s “Vehicle Analysis Analytics”, IBM Watson Content. Processed data becomes the corpus of information which is interrogated to provide examples of finding data for mass regression analysis, technology impacts on MPE (Mass Properties Engineering), mass properties control, standards, and other aspects of interest.

Cerro, Jeffrey A.↗
DOE OSTI2022

Improving the Performance of DGEMM with MoA and Cache-Blocking: Preprint

The goal of this paper is to demonstrate performance enhancements of the high performance dense linear algebra matrix-matrix multiply DGEMM kernel, widely implemented by vendors in the basic linear algebra subroutine BLAS library. The mathematics of arrays (MoA) paradigm due to Mullin (1988) results in contiguous memory accesses in combination with Church-Rosser complete language constructs optimized for target processor architectures [3]. Our performance studies demonstrate that the MoA implementation of DGEMM combined with optimal cache-blocking strategies results in at least a 25% performance gain on both Intel Xeon Skylake and IBM Power-9 processors over the vendor supplied Intel MKL and IBM ESSL basic linear algebra libraries. Results are presented for the NREL Eagle and ORNL Summit supercomputers.

cache-blocking↗
DOE OSTI2025

Quantum approximate multi-objective optimization

The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, that is, the set of all Pareto-optimal solutions, where no objective can be improved without degrading another one. Multi-objective optimization can be challenging classically, even if the corresponding single-objective optimization problems are efficiently solvable. Thus, multi-objective optimization represents a compelling problem class to analyze with quantum computers. Here we use a low-depth quantum approximate optimization algorithm to approximate the optimal Pareto front of certain multi-objective weighted maximum-cut problems. We demonstrate its performance on an IBM Quantum computer, as well as with matrix product state numerical simulation, and show its potential to outperform classical approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1965

Paper tape conversion procedures

Implementation of programs for conversion of paper tape information to IBM 7090/94 magnetic tape, punched cards, and printed page

MAGNETIC TAPE↗
DOE OSTI2024

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

Machine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Sparse non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations

The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characterization and modeling can provide key insights into the effect of noise on quantum algorithms and inform the design of targeted error protection protocols. However, constructing effective noise models that are sparse in model parameters, yet predictive can be challenging. In this work, we present an approach for effective noise modeling of multi-qubit operations on transmon-based devices. Through a comprehensive characterization of seven devices offered by the IBM Quantum Platform, we show that the model can capture and predict a wide range of single- and two-qubit behaviors, including non-Markovian effects resulting from spatiotemporally correlated noise sources. The model’s predictive power is further highlighted through multi-qubit dynamical decoupling demonstrations and an implementation of the variational quantum eigensolver. As a training proxy for the hardware, we show that the model can predict expectation values within a relative error of 0.5%; this is a sevenfold improvement over default hardware noise models. Through these demonstrations, we highlight key error sources in superconducting qubits and illustrate the utility of reduced noise models for predicting hardware dynamics.

open quantum systems & decoherence↗