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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Development of a three-dimensional Navier-Stokes code on CDC star-100 computer

A three-dimensional code in body-fitted coordinates was developed using MacCormack's algorithm. The code is structured to be compatible with any general configuration, provided that the metric coefficients for the transformation are available. The governing equations are developed in primitive variables in order to facilitate the incorporation of physical boundary conditions and turbulence-closure models. MacCormack's two-step, unsplit, time-marching algorithm is used to solve the unsteady Navier-Stokes equations until steady-state solution is achieved. Cases discussed include (1) flat plate in supersonic free stream; (2) supersonic flow along an axial corner; (3) subsonic flow in an axial corner at M infinity = 0.95; and (4) supersonic flow in an axial corner at M infinity 1.5.

Vatsa, V. N.↗
NASA NTRS1980

Segmentation, dynamic storage, and variable loading on CDC equipment

Techniques for varying the segmented load structure of a program and for varying the dynamic storage allocation, depending upon whether a batch type or interactive type run is desired, are explained and demonstrated. All changes are based on a single data input to the program. The techniques involve: code within the program to suppress scratch pad input/output (I/O) for a batch run or translate the in-core data storage area from blank common to the end-of-code+1 address of a particular segment for an interactive run; automatic editing of the segload directives prior to loading, based upon data input to the program, to vary the structure of the load for interactive and batch runs; and automatic editing of the load map to determine the initial addresses for in core data storage for an interactive run.

Tiffany, S. H.↗
NASA NTRS1981

Contextual classification on a CDC Flexible Processor system

A potential hardware organization for the Flexible Processor Array is presented. An algorithm that implements a contextual classifier for remote sensing data analysis is given, along with uniprocessor classification algorithms. The Flexible Processor algorithm is provided, as are simulated timings for contextual classifiers run on the Flexible Processor Array and another system. The timings are analyzed for context neighborhoods of sizes three and nine.

Smith, B. W.↗
NASA NTRS1982

Survey of new vector computers: The CRAY 1S from CRAY research; the CYBER 205 from CDC and the parallel computer from ICL - architecture and programming

Problems which can arise with vector and parallel computers are discussed in a user oriented context. Emphasis is placed on the algorithms used and the programming techniques adopted. Three recently developed supercomputers are examined and typical application examples are given in CRAY FORTRAN, CYBER 205 FORTRAN and DAP (distributed array processor) FORTRAN. The systems performance is compared. The addition of parts of two N x N arrays is considered. The influence of the architecture on the algorithms and programming language is demonstrated. Numerical analysis of magnetohydrodynamic differential equations by an explicit difference method is illustrated, showing very good results for all three systems. The prognosis for supercomputer development is assessed.

Gentzsch, W.↗
NASA NTRS1984

Adapting iterative algorithms for solving large sparse linear systems for efficient use on the CDC CYBER 205

Adapting and designing mathematical software to achieve optimum performance on the CYBER 205 is discussed. Comments and observations are made in light of recent work done on modifying the ITPACK software package and on writing new software for vector supercomputers. The goal was to develop very efficient vector algorithms and software for solving large sparse linear systems using iterative methods.

Kincaid, D. R.↗
NASA NTRS1984

Vectorized multigrid Poisson solver for the CDC CYBER 205

The full multigrid (FMG) method is applied to the two dimensional Poisson equation with Dirichlet boundary conditions. This has been chosen as a relatively simple test case for examining the efficiency of fully vectorizing of the multigrid method. Data structure and programming considerations and techniques are discussed, accompanied by performance details.

Barkai, D.↗
NASA NTRS1992

The 32nd CDC: System identification using interval dynamic models

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Keel, L. H.↗
NASA NTRS1989

The 32nd CDC: Robust stabilizer synthesis for interval plants using Nevanlina-pick theory

The synthesis of robustly stabilizing compensators for interval plants, i.e., plants whose parameters vary within prescribed ranges is discussed. Well-known H(sup infinity) methods are used to establish robust stabilizability conditions for a family of plants and also to synthesize controllers that would stabilize the whole family. Though conservative, these methods give a very simple way to come up with a family of robust stabilizers for an interval plant.

Bhattacharya, Saikat↗
DOE OSTI2021

Mapping the combustion modes of a dual-fuel compression ignition engine

Compression-ignition (CI) engines can produce higher thermal efficiency (TE) and thus lower carbon dioxide (CO 2 ) emissions than spark-ignition (SI) engines. Unfortunately, the overall fuel economy of CI engine vehicles is limited by their emissions of nitrogen oxides (NO x ) and soot, which must be mitigated with costly, resource- and energy-intensive aftertreatment. NO x and soot could also be mitigated by adding premixed gasoline to complement the conventional, non-premixed direct injection (DI) of diesel fuel in CI engines. Several such “dual-fuel” combustion modes have been introduced in recent years, but these modes are usually studied individually at discrete conditions. This paper introduces a mapping system for dual-fuel CI modes that links together several previously studied modes across a continuous two-dimensional diagram. This system includes the conventional diesel combustion (CDC) and conventional dual-fuel (CDF) modes; the well-explored advanced combustion modes of HCCI, RCCI, PCCI, and PPCI; and a previously discovered but relatively unexplored combustion mode that is herein titled “Piston-split Dual-Fuel Combustion” or PDFC. Tests show that dual-fuel CI engines can simultaneously increase TE and lower NO x and/or soot emissions at high loads through the use of Partial HCCI (PHCCI). At low loads, PHCCI is not possible, but either PDFC or RCCI can be used to further improve NO x and/or soot emissions, albeit at slightly lower TE. These results lead to a “partial dual-fuel” multi-mode strategy of PHCCI at high loads and CDC at low loads, linked together by PDFC. Drive cycle simulations show that this strategy, when tuned to balance NO x and soot reductions, can reduce engine-out CO 2 emissions by about 1% while reducing NO x and soot by about 20% each with respect to CDC. This increases emissions of unburnt hydrocarbons (UHC), still in a treatable range (2.0 g/kWh) but five times as high as CDC, requiring changes in aftertreatment strategy.

Engineering↗
NASA NTRS2012

Using NASA Environmental Data to Enhance Public Health Decision Making

The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗
DOE OSTI2021

Integration of Complete Plasmids Containing Bont Genes into Chromosomes of Clostridium parabotulinum , Clostridium sporogenes , and Clostridium argentinense

At least 40 toxin subtypes of botulinum neurotoxins (BoNTs), a heterogenous group of bacterial proteins, are produced by seven different clostridial species. A key factor that drives the diversity of neurotoxigenic clostridia is the association of bont gene clusters with various genomic locations including plasmids, phages and the chromosome. Analysis of Clostridium sporogenes BoNT/B1 strain CDC 1632, C. argentinense BoNT/G strain CDC 2741, and Clostridium parabotulinum BoNT/B1 strain DFPST0006 genomes revealed bont gene clusters within plasmid-like sequences within the chromosome or nested in large contigs, with no evidence of extrachromosomal elements. A nucleotide sequence (255,474 bp) identified in CDC 1632 shared 99.5% identity (88% coverage) with bont/B1-containing plasmid pNPD7 of C. sporogenes CDC 67071; CDC 2741 contig AYSO01000020 (1.1 MB) contained a ~140 kb region which shared 99.99% identity (100% coverage) with plasmid pRSJ17_1 of C. argentinense BoNT/G strain 89G; and DFPST0006 contig JACBDK0100002 (573 kb) contained a region that shared 100% identity (99%) coverage with the bont/B1-containing plasmid pCLD of C. parabotulinum Okra. This is the first report of full-length plasmid DNA-carrying complete neurotoxin gene clusters integrated in three distinct neurotoxigenic species: C. parabotulinum, C. sporogenes and C. argentinense.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Ducted Fuel Injection vs. Conventional Diesel Combustion: Extending the Load Range in an Optical Engine with a Four-Orifice Fuel Injector

Ducted fuel injection (DFI) is a technique to attenuate soot formation in compression ignition engines relative to conventional diesel combustion (CDC). The concept is to inject fuel through a small tube inside the combustion chamber to reduce equivalence ratios in the autoignition zone relative to CDC. DFI has been studied at loads as high as 8.5 bar gross indicated mean effective pressure (IMEP g ) and as low as 2.5 bar IMEP g using a four-orifice fuel injector. Across previous studies, DFI has been shown to attenuate soot emissions, increase NO x emissions (at constant charge dilution), and slightly decrease fuel conversion efficiencies for most tested points. This study expands on the previous work by testing 1.1 bar IMEPg (low-load/idle) conditions and 10 bar IMEPg (higher-load) conditions with the same four-orifice fuel injector, as well as examining potential causes of the degradations in NO x emissions and fuel conversion efficiencies. DFI and CDC are directly compared at each operating point in the study. At the low-load condition, the intake charge dilution was swept to elucidate the soot and NO x performance of DFI. The low-load range is important because it is the target of impending, more-stringent emissions regulations, and DFI is shown to be a potentially effective approach for helping to meet these regulations. The results also indicate that DFI likely has slightly decreased fuel conversion efficiencies relative to CDC. We find that the increase in NO x emissions with DFI is likely due to longer charge gas residence times at higher temperatures, which arise from shorter combustion durations and advanced combustion phasing relative to CDC.

42 ENGINEERING↗
NASA NTRS1990

Cable delay compensator for microwave signal distribution over optical fibers

The basic principles of microwave fiber-optic systems are outlined with emphasis on fiber-optic cable delay compensators (CDC). Degradation of frequency and phase stability is considered, and it is pointed out that the long-term stability of a fiber-optic link is degraded by group delay variations due to temperature fluctuations in the optical fiber and low-frequency noise characteristics of the laser. A CDC employing a voltage-controlled oscillator to correct for phase variations in the optical fiber is presented, and the static as well as dynamic closed-loop analyses of the fiber-optic CDC are discussed. A constructed narrow-band fiber-optic CDC is shown to reduce phase variations caused by temperature fluctuations by a factor of 400. A wide-band CDC utilizing a temperature-controlled coil of fiber to compensate for phase delay is also proposed.

Primas, Lori E.↗
NASA NTRS2013

It's the Heat AND the Humidity -- Assessment of Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. In a NASA-funded project supporting the National Climate Assessment, we are providing historical and future measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The project s emphasis is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM output, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons, 2040 and 2090, are the focus of future assessments; these are compared to the recent past period of 1981-2000. We are characterizing regional-scale temperature and humidity conditions using GCM output for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM output have been analyzed to develop a heat stress climatology based on statistics of extreme heat indicators. Differences between the two future and past periods have been used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes, combined with hourly historical meteorological data at a spatial scale (12 km) much finer than that of GCMs, enable us to create future climate realizations, from which we compute the daily heat stress measures and related spatially-specific climatological fields. These include the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. All output is being provided at the 12 km spatial scale and will also be aggregated to the county level, which is a popular scale of analysis for public health researchers. County-level statistics will be made available by our collaborators at the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. CDC WONDER makes the information resources of the CDC available to public health professionals and the general public. This addition of heat stress measures to CDC WONDER will allow decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. It will also allow public health researchers and policy makers to better include such heat stress measures in the context of national health data available in the CDC WONDER system. The users will be able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Crosson, William L↗