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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

International Space Station Major Constituent Analyzer On-orbit Performance

The Major Constituent Analyzer (MCA) is a mass spectrometer based system that measures the major atmospheric constituents on the International Space Station. A number of limited-life components require periodic change-out, including the ORU 02 analyzer and the ORU 08 Verification Gas Assembly. The most recent ORU 02 and ORU 08 assemblies are operating nominally. For ORU 02, the ion source filaments and ion pump lifetime continue to be key determinants of MCA performance. Additionally, testing is underway to evaluate the capacity of the MCA to analyze ammonia. Finally, plans are being made to bring the second MCA on ISS to an operational configuration.

Gardner, Ben D.↗
NASA NTRS2017

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA technology and infrastructure have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences in 2014, 2015, and 2016. In the summer school, the students work on group research projects where CMDA provide datasets, analysis tools, and provenance support utility tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each of the science use cases is described in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered, an analysis result such as output plots and data files, and a link to the corresponding analysis service call with all the input arguments filled.

Bao, Qihao↗
NASA NTRSDate not supplied

Py MILab: Capturing, Analyzing and Storing Test Data

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.

Data management↗
NASA NTRS2002

Science opportunity analyzer - a multi-mission tool for planning

For many years the diverse scientific community that supports JPL's wide variety ofinterplanetary space missions has needed a tool in order to plan and develop their experiments. The tool needs to be easily adapted to various mission types and portable to the user community. The Science Opportunity Analyzer, SOA, now in its third year of development, is intended to meet this need. SOA is a java-based application that is designed to enable scientists to identify and analyze opportunities for science observations from spacecraft. It differs from other planning tools in that it does not require an in-depth knowledge of the spacecraft command system or operation modes to begin high level planning. Users can, however, develop increasingly detailed levels of design. SOA consists of six major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, Data Output and Communications. Opportunity Search is a GUI driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. The user is given extensive flexibility to customize what is displayed in the view. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily impact the cost to science if flight rule changes occur. Data Output generates information based on the spacecraft's trajectory, opportunity search results or based on a created observation. The data can be viewed either in tabular format or as a graph. Finally, SOA is unique in that it is designed to be able to communicate with a variety of existing planning and sequencing tools. From the very beginning SOA was designed with the user in mind. Extensive surveys of the potential user community were conducted in order to develop the software requirements. Throughout the development period, close ties have been maintained with the science community to insure that the tool maintains its user focus. Although development is still in its early stages, SOA is already developing a user community on the Cassini project, which is depending on this tool for their science planning. There are other tools at JPL that do various pieces of what SOA can do; however, there is no other tool which combines all these functions and presents them to the user in such a convenient, cohesive, and easy to use fashion.

SOA science planning mission operations sequence s↗
NASA NTRS2006

On-wafer vector network analyzer measurements in the 220-325 Ghz frequency band

We report on a full two-port on-wafer vector network analyzer test set for the 220-325 GHz (WR3) frequency band. The test set utilizes Oleson Microwave Labs frequency extenders with the Agilent 8510C network analyzer. Two port on-wafer measurements are made with GGB Industries coplanar waveguide (CPW) probes. With this test set we have measured the WR3 band S-parameters of amplifiers on-wafer, and the characteristics of the CPW wafer probes. Results for a three stage InP HEMT amplifier show 10 dB gain at 235 GHz [1], and that of a single stage amplifier, 2.9 dB gain at 231 GHz. The approximate upper limit of loss per CPW probe range from 3.0 to 4.8 dB across the WR3 frequency band.

coplanar waveguides↗
NASA NTRS2015

Design and Demonstration of the Mars Organic Molecule Analyzer (MOMA) on the ExoMars 2018 Rover

The Mars Organic Molecule Analyzer (MOMA) investigation is a key astrobiology experiment scheduled to launch on the joint ESA-Roscosmos ExoMars 2018 rover mission. MOMA will examine the chemical composition of geological samples acquired from depths of up to two meters below the martian surface, where fragile organic molecules may be protected from destructive cosmic radiation and/or oxidative chemical reactions. The heart of the MOMA mass spectrometer subsystem (i.e., MOMA-MS) is a miniaturized linear ion trap (LIT) that supports two distinct modes of operation to detect: i) volatile and semi-volatile, low-to-moderate mass organics (less than or equal to 500 Da) via pyrolysis coupled with gas chromatography mass spectrometry (pyr/GCMS); and, ii) more refractory, moderate-to-high mass compounds (up to 1000 Da) via laser desorption (LDMS) at ambient Mars pressures. Additionally, the LIT mass analyzer enables selective ion trapping via multi-frequency waveform ion excitation (e.g., stored waveform inverse Fourier transform, or SWIFT), and structural characterization of complex molecules using tandem mass spectrometry (MSIMS). A high-fidelity Engineering Test Unit (ETU) of MOMAMS, including the LIT subassembly, dual-gun electron ionization source, micropirani pressure gauge, solenoid-driven aperture valve, redundant detection chains, and control electronics, has been built and tested at NASA GSFC under relevant operational conditions (pressure, temperature, etc.). Spaceflight qualifications of individual hardware components and integrated subassemblies have been validated through vibration, shock, thermal, lifetime, and performance evaluations. The ETU serves as a pathfinder for the flight model buildup, integration and test, as the ETU meets the form, fit and function of the flight unit that will be delivered to MPS in late 2015. To date, the ETU of MOMA-MS has been shown to meet or exceed all functional requirements, including mass range, resolution, accuracy, instrumental drift, and Iimit-of-detection specifications, thereby enabling the primary science objectives of the MOMA investigation and ExoMars 2018 mission.

Mars Organic Molecule Analyzer↗
NASA NTRS1996

The Cosmic Dust Analyzer for Cassini

The Cosmic Dust Analyzer (CDA) is designed to characterize the dust envirenment in interplanetary space, in the Jovian and in the Saturnian systems. The instrument consists of two major components, the Dust Analyzer (DA) and the HIgh Rate Detector(HRD).

cosmic↗
NASA NTRS1968

Welder analyzer

Welder analyzer circuit evaluates and certifies resistance welding machines. The analyzer measures peak current, peak voltage, peak power, total energy, and first-pulse energy. It is used as an energy monitor while welding is being performed, or a precision shunt load for a pure electrical evaluation of the weld machine.

Miller, L. L.↗
NASA NTRS1971

To develop a spectral analyzer for physiological and medical use

Scientific requirements necessary to develop a spectral analyzer for monitoring mammalian subjects, are discussed. The analyzer measures dynamic or time dependent data as a measure of the subjects operating status. Measurable data include metabolic rate, body temperature, and blood constituents like glucose, oxygen, and carbon dioxide, and lactic acid. Metabolic cycles were found with periodicities in the range of minutes and hours; longer cycles in body weight (3 1/2 days and 60 days), indicative of metabolic processes, were also found.

Iberall, A.↗
NASA NTRS1971

Methods of analyzing microsize particulate aerosols and contaminants.

Methods for analyzing atmospheric aerosols and particulate contaminants from natural and industrial sources are briefly described. Their development was spurred by interest in cosmic dust investigations. These methods make it possible to analyze individual particles ranging in size from 0.01 to 1000 microns for elemental and mineralogical content. The described methods include transmission electron microscopy, scanning electron microscopy, electron probe microanalysis, X-ray diffraction, optical mineralogy, and density measurement.

Blanchard, M. B.↗
NASA NTRS1972

A quadratic pulse height analyzer for space applications.

A flight-worthy pulse height analyzer that has a quadratic transfer function is described. This quadratic function permits optimum usage of the entire PHA dynamic range due to the quadratic nature of the gamma ray spectrometer's resolution vs energy. After the theoretical design discussion, the implementation of the design is examined and test results described. The analyzer is part of the University of New Hampshire gamma ray monitor for OSO-H.

Burtis, D. W.↗
NASA NTRS1972

Modular thermal analyzer routine, volume 1

The Modular Thermal Analyzer Routine (MOTAR) is a general thermal analysis routine with strong capabilities for performing thermal analysis of systems containing flowing fluids, fluid system controls (valves, heat exchangers, etc.), life support systems, and thermal radiation situations. Its modular organization permits the analysis of a very wide range of thermal problems for simple problems containing a few conduction nodes to those containing complicated flow and radiation analysis with each problem type being analyzed with peak computational efficiency and maximum ease of use. The organization and programming methods applied to MOTAR achieved a high degree of computer utilization efficiency in terms of computer execution time and storage space required for a given problem. The computer time required to perform a given problem on MOTAR is approximately 40 to 50 percent that required for the currently existing widely used routines. The computer storage requirement for MOTAR is approximately 25 percent more than the most commonly used routines for the most simple problems but the data storage techniques for the more complicated options should save a considerable amount of space.

Oren, J. A.↗
NASA NTRS1973

The effect of grid transparency and finite collector size on determining ion temperature and density by the retarding potential analyzer

The analysis of ion data from retarding potential analyzers (RPA's) is generally done under the planar approximation, which assumes that the grid transparency is constant with angle of incidence and that all ions reaching the plane of the collectors are collected. These approximations are not valid for situations in which the ion thermal velocity is comparable to the vehicle velocity, causing ions to enter the RPA with high average transverse velocity. To investigate these effects, the current-voltage curves for H+ at 4000 K were calculated, taking into account the finite collector size and the variation of grid transparency with angle. These curves are then analyzed under the planar approximation. The results show that only small errors in temperature and density are introduced for an RPA with typical dimensions; and that even when the density error is substantial for non-typical dimensions, the temperature error remains minimal.

Troy, B. E., Jr.↗
NASA NTRS1973

Counter tube window and X-ray fluorescence analyzer study

A study was performed to determine the best design tube window and X-ray fluorescence analyzer for quantitative analysis of Venusian dust and condensates. The principal objective of the project was to develop the best counter tube window geometry for the sensing element of the instrument. This included formulation of a mathematical model of the window and optimization of its parameters. The proposed detector and instrument has several important features. The instrument will perform a near real-time analysis of dust in the Venusian atmosphere, and is capable of measuring dust layers less than 1 micron thick. In addition, wide dynamic measurement range will be provided to compensate for extreme variations in count rates. An integral pulse-height analyzer and memory accumulate data and read out spectra for detail computer analysis on the ground.

Hertel, R.↗
NASA NTRS1973

Primary electron spectrometer, 18:63 UE: Electrostatic analyzer description and energy spectrum determination

The primary electron spectrometer used to detect auroral electrons on sounding rocket 18:63 UE is described. The spectrometer used exponentially decaying positive and negative voltages applied to spherical deflection plates for energy analysis. A method for determining the analyzer response which does not require the assumptions that the ratio of plate separation to mean radius, the entrance or the exit apertures are small is described. By comparison with experiment it is shown that the effect of neither entrance nor exit collimation can be ignored. The experimental and calculated values of the limiting orbits agree well. A non-iterative technique of unfolding the electron differential energy spectrum is described. This method does not require the usual assumption of a flat or histogram-type energy spectrum. The unfolded spectra using both this technique and one which assumes a flat spectrum are compared to actual input spectra. This technique is especially useful in analyzing peaked auroral electron energy spectra.

Pongratz, M. B.↗