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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Pharmacokinetics of Scopolamine Intranasal Gel Formulation (INSCOP) During Antiorthostatic Bedrest

Space Motion Sickness (SMS) is experienced during early flight days of space missions and on reduced gravity simulation flights which require treatment with medications. Oral administration of scopolamine tablets is still a common practice to prevent SMS symptoms. Bioavailability of medications taken by mouth for SMS is often low and variable. Intranasal (IN) administration of medications has been reported to achieve higher and more reliable bioavailability than from an equivalent oral dose. In this FDA reviewed phase II clinical trial, we evaluated pharmacokinetics of an investigative new drug formulation, INSCOP during ambulatory (AMB) and antiorthostatic bedrest (HBR), a ground-based microgravity analog. Twelve subjects including 6 males and 6 females received 0.2 and 0.4 mg doses of INSCOP on separate days during AMB and ABR in a randomized, double blind cross over experimental design. Blood samples were collected at regular time intervals for 24 h post dose and analyzed for free scopolamine concentrations by an LC-MS-MS method. Pharmacokinetic parameters were calculated using concentration versus time data and compared between AMB and ABR conditions. Results indicated that maximum concentration and relative bioavailability increased marginally during ABR compared to AMB; differences in PK parameters between AMB and ABR were greater with 0.2 mg than with 0.4 mg dose. Gender specific differences in PK parameters was observed both during AMB and ABR with differences higher in females between the two conditions than in males. A significant observation is that while gender differences in PK appear to exist, the differences in primary PK parameters between AMB and ABR after IN administration, unlike oral administration, are minimal and may not be clinically significant for both genders.

Putcha, L.↗
NASA NTRS2012

Alternative Fuels Research Laboratory

NASA Glenn has invested over $1.5 million in engineering, and infrastructure upgrades to renovate an existing test facility at the NASA Glenn Research Center (GRC), which is now being used as an Alternative Fuels Laboratory. Facility systems have demonstrated reliability and consistency for continuous and safe operations in Fischer-Tropsch (F-T) synthesis and thermal stability testing. This effort is supported by the NASA Fundamental Aeronautics Subsonic Fixed Wing project. The purpose of this test facility is to conduct bench scale F-T catalyst screening experiments. These experiments require the use of a synthesis gas feedstock, which will enable the investigation of F-T reaction kinetics, product yields and hydrocarbon distributions. Currently the facility has the capability of performing three simultaneous reactor screening tests, along with a fourth fixed-bed reactor for catalyst activation studies. Product gas composition and performance data can be continuously obtained with an automated gas sampling system, which directly connects the reactors to a micro-gas chromatograph (micro GC). Liquid and molten product samples are collected intermittently and are analyzed by injecting as a diluted sample into designated gas chromatograph units. The test facility also has the capability of performing thermal stability experiments of alternative aviation fuels with the use of a Hot Liquid Process Simulator (HLPS) (Ref. 1) in accordance to ASTM D 3241 "Thermal Oxidation Stability of Aviation Fuels" (JFTOT method) (Ref. 2). An Ellipsometer will be used to study fuel fouling thicknesses on heated tubes from the HLPS experiments. A detailed overview of the test facility systems and capabilities are described in this paper.

Surgenor, Angela D.↗
NASA NTRS2012

NASA Tech Briefs, March 2012

The topics include: 1) Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy; 2) Portable Fourier Transform Spectroscopy for Analysis of Surface Contamination and Quality Control; 3) In Situ Geochemical Analysis and Age Dating of Rocks Using Laser Ablation-Miniature Mass Spectrometer; 4) Physics Mining of Multi-Source Data Sets; 5) Photogrammetry Tool for Forensic Analysis; 6) Connect Global Positioning System RF Module; 7) Simple Cell Balance Circuit; 8) Miniature EVA Software Defined Radio; 9) Remotely Accessible Testbed for Software Defined Radio Development; 10) System-of-Systems Technology-Portfolio-Analysis Tool; 11) VESGEN Software for Mapping and Quantification of Vascular Regulators; 12) Constructing a Database From Multiple 2D Images for Camera Pose Estimation and Robot Localization; 13) Adaption of G-TAG Software for Validating Touch and Go Asteroid Sample Return Design Methodology; 14) 3D Visualization for Phoenix Mars Lander Science Operations; 15) RxGen General Optical Model Prescription Generator; 16) Carbon Nanotube Bonding Strength Enhancement Using Metal Wicking Process; 17) Multi-Layer Far-Infrared Component Technology; 18) Germanium Lift-Off Masks for Thin Metal Film Patterning; 19) Sealing Materials for Use in Vacuum at High Temperatures; 20) Radiation Shielding System Using a Composite of Carbon Nanotubes Loaded With Electropolymers; 21) Nano Sponges for Drug Delivery and Medicinal Applications; 22) Molecular Technique to Understand Deep Microbial Diversity; 23) Methods and Compositions Based on Culturing Microorganisms in Low Sedimental Fluid Shear Conditions; 24) Secure Peer-to-Peer Networks for Scientific Information Sharing; 25) Multiplexer/Demultiplexer Loading Tool (MDMLT); 26) High-Rate Data-Capture for an Airborne Lidar System; 27) Wavefront Sensing Analysis of Grazing Incidence Optical Systems; 28) Foam-on-Tile Damage Model; 29) Instrument Package Manipulation Through the Generation and Use of an Attenuated-Fluent Gas Fold; 30) Multicolor Detectors for Ultrasensitive Long-Wave Imaging Cameras; 31) Lunar Reconnaissance Orbiter (LRO) Command and Data Handling Flight Electronics Subsystem; and 32) Electro-Optic Segment-Segment Sensors for Radio and Optical Telescopes.

Source record↗
NASA NTRS2012

Post Flight Analysis of Optical Specimens from MISSE7

More than 100 optical specimens were flown on the MISSE7 platform. These included bare substrates in addition to coatings designed to exhibit clearly defined or enhanced sensitivity to the accumulation of contamination. Measurements were performed using spectrophotometers operating from the UV through the IR as well as ellipsometry. Results will be presented in addition to discussion of the best options for design of samples for future exposure experiments.

Stewart, Alan F.↗
NASA NTRS2013

NASA Tech Briefs, December 2013

Topics include: Microwave Kinetic Inductance Detector With; Selective Polarization Coupling; Flexible Microstrip Circuits for; Superconducting Electronics; CFD Extraction Tool for TecPlot From DPLR Solutions; RECOVIR Software for Identifying Viruses; Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model; Orbital Debris Engineering Model (ORDEM) v.3; Scatter-Reducing Sounding Filtration Using a Genetic Algorithm and Mean Monthly Standard Deviation; Thermo-Mechanical Methodology for Stabilizing Shape Memory Alloy Response; Hermetic Seal Designs for Sample Return Sample Tubes; Silicon Alignment Pins: An Easy Way To Realize a Wafer-to-Wafer Alignment; Positive-Buoyancy Rover for Under Ice Mobility; Electric Machine With Boosted Inductance to Stabilize Current Control; International Space Station-Based Electromagnetic Launcher for Space Science Payloads; Advanced Hybrid Spacesuit Concept Featuring Integrated Open Loop and Closed Loop Ventilation Systems; Data Quality Screening Service.

Source record↗
NASA NTRS2015

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Spacecraft Systems↗
NASA NTRS2015

The Two-Column Aerosol Project: Phase I - Overview and Impact of Elevated Aerosol Layers on Aerosol Optical Depth

The Two-Column Aerosol Project (TCAP), conducted from June 2012 through June 2013, was a unique study designed to provide a comprehensive data set that can be used to investigate a number of important climate science questions, including those related to aerosol mixing state and aerosol radiative forcing. The study was designed to sample the atmosphere be tween and within two atmospheric columns; one fixed near the coast of North America (over Cape Cod, MA) and a second moveable column over the Atlantic Ocean several hundred kilometers from the coast. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was deployed at the base of the Cape Cod column, and the ARM Aerial Facility was utilized for the summer and winter intensive observation periods. One important finding from TCAP is that four of six nearly cloud-free flight days had aerosol layers aloft in both the Cape Cod and maritime columns that were detected using the nadir pointing second-generation NASA high-spectral resolution lidar (HSRL-2).These layer s contributed up to 60 of the total observed aerosol optical depth (AOD). Many of these layers were also intercepted by the aircraft configured for in situ sampling, and the aerosol in the layers was found to have increased amounts of biomass burning material and nitrate compared to aerosol found near the surface. In addition, while there was a great deal of spatial and day-to-day variability in the aerosol chemical composition and optical properties, no systematic differences between the two columns were observed.

aerosols↗
NASA NTRS2018

Dragonfly: Investigating the Surface Composition of Titan

Dragonfly is a rotorcraft lander mission, selected as a finalist in NASA's New Frontiers Program, that is designed to sample materials and determine the surface composition in different geologic settings on Titan. This revolutionary mission concept would explore diverse locations to characterize the habitability of Titan's environment, to investigate how far prebiotic chemistry has progressed, and to search for chemical signatures that could be indicative of water-based and/or hydrocarbon-based life. Here we describe Dragonfly's capabilities to determine the composition of a variety of surface units on Titan, from elemental components to complex organic molecules. The compositional investigation ncludes characterization of local surface environments and finely sampled materials. The Dragonfly flexible sampling approach can robustly accommodate materials from Titan's most intriguing surface environments.

Trainer, M. G.↗
NASA NTRSDate not supplied

A Study of the Kinematic and Dynamic Processes associated with Mesoscale Snowbands in a Midwest United States Snowstorm on 5 February 2020

Mesoscale snowbands are frequently responsible for dumping large amounts of snow over a relatively small region and pose a considerable forecasting challenge. The present study focuses on the mesoscale snowbands observed on 5 February 2020 over the Midwest United States. In order to improve the forecasting of mesoscale snowbands, it is important to understand the processes that organize these features. To this end, we are investigating the kinematic and dynamic processes that shape the mesoscale snowbands using a combination of high-altitude airborne radar, in-situ aircraft measurements, surface observations, and model simulations. This presentation will primarily focus on the three-dimensional structure of the mesoscale snowbands in terms of EXRAD reflectivity and horizontal winds. The horizontal winds are retrieved from the EXRAD scanning-beam Doppler velocities using a VAD technique. These winds, in combination with virtual potential temperature taken from the HRRR operational analysis, are used to compute the bulk Richardson number in order to investigate any contributions to banding features that might be tied to Kelvin-Helmholtz instability. Our analysis does not support the presence of Kelvin-Helmholtz instability. That said, low, but not subcritical, Richardson numbers were found near the top of some banding features in this event and it is possible that the model analysis is smoothing out the virtual potential temperature fields. Finally, we present arguments for using a flight module designed to sample the along-band structures with particular emphasis on observing generating cells and low-level microphysics.

Charles N. Helms↗
NASA NTRSDate not supplied

NASA’s Ground-Based Microgravity Simulation Facility

Since opportunities to conduct experiments in space are scarce, various microgravity simulators and analogs have been widely used in space biology ground studies. Even though microgravity simulators do not produce all of the biological effects observed in the true microgravity environment, they provide alternative test platforms that are effective, affordable, and readily available to facilitate microgravity research. The Microgravity Simulation Support Facility (MSSF) at the National Aeronautics and Space Administration (NASA) John F. Kennedy Space Center (KSC) has been established for conducting short duration experiments, typically less than 1 month, utilizing a variety of microgravity simulation devices for research at different gravity levels. The simulators include, but are not limited to, 2D Clinostats, 3D Clinostats, Random Positioning Machines, and Rotating Wall Vessels. In this presentation, we will provide an overview of current MSSF capabilities, research conducted in MSSF, new technology developments, customized sample holder designs, and the physical characteristics of these microgravity simulators.

Ye Zhang↗
NASA NTRSDate not supplied

NASA's Ground-Based Microgravity Simulation Facility

Since opportunities to conduct experiments in space are scarce, various microgravity simulators and analogs have been widely used in space biology ground studies. Even though microgravity simulators do not produce all of the biological effects observed in the true microgravity environment, they provide alternative test platforms that are effective, affordable, and readily available to facilitate microgravity research. The Microgravity Simulation Support Facility (MSSF) at the National Aeronautics and Space Administration (NASA) John F. Kennedy Space Center (KSC) has been established for conducting short duration experiments, typically less than 1 month, utilizing a variety of microgravity simulation devices for research at different gravity levels. The simulators include, but are not limited to, 2D Clinostats, 3D Clinostats, Random Positioning Machines, and Rotating Wall Vessels. In this chapter, we will discuss current MSSF capabilities, development concepts, customized sample holder designs, and the physical characteristics of these microgravity simulators.

Ye Zhang↗
NASA NTRSDate not supplied

Surface-Enhanced Raman Spectroscopy (SERS) for Venus Atmospheric Characterization and Trace Organics Identification

The chemical composition of Venus’ clouds has attracted considerable attention since the first atmospheric probes entered the planet in the early 1980s. We know from these early studies that cloud aerosols consist of micron and submicron droplets of sulfuric acid (~85%) and water (~15%), but outstanding questions still exist regarding their trace chemical composition, especially the identity of the unknown UV absorber(s) and the possible presence of organic material (Spacek & Benner, 2021). Veritas and DaVinci will launch at the end of the decade to advance our understanding of Venus’ surface and atmosphere, however these missions are not designed to sample and analyze cloud aerosols at trace levels. Raman spectroscopy is an ideal candidate for characterizing aerosols and has a track record in studies of Earth’s atmosphere. Raman provides broad chemical screening of organic and inorganic molecules within seconds, and Raman instrumentation can be miniaturized to meet the stringent size, weight, and power (SWaP) constraints of an atmospheric probe while retaining sensitivity and specificity that allow spectral fingerprinting of compounds and functional groups. Raman alone is insufficient to characterize trace constituents however, as limits of detection are typically in the 10s to 100s of ppm at best for planetary Raman spectrometers. Surface-Enhanced Raman Spectroscopy (SERS), a method that utilizes nanoscale materials interacting with a sample to enhance weak Raman signals by orders of magnitude when probed by a laser, has the potential to drastically improve the sensitivity of a Raman instrument while adding little-to-no SWaP nor complexity to a scientific payload. We aim to de-risk SERS technology for rapid infusion in future planetary science missions by developing custom SERS substrates tailored towards Venus atmospheric exploration. We have developed SERS substrates consisting of nanotextured silver films and tested them on potential Venus-relevant organic compounds, with preliminary results indicating sub-ppm sensitivity. Additionally, we have planned tests to investigate durability, longevity, and other key characteristics that impact SERS substrate performance to prepare SERS technology for flight.

Venus↗
NASA NTRS2022

Flight Software Dictionary Development for the Mars2020 Rover

The Mars2020 project, developed and operated by the Jet Propulsion Laboratory (JPL), successfully landed the Perseverance rover and its flying companion Ingenuity on the surface of Mars on February 18th 2021. Perseverance combines heritage and cutting-edge flight software and hardware to accomplish crucial mission requirements related to Martian surface sampling. The design, development, and operation of NASA’s large strategic science missions require the ability to communicate spacecraft capabilities to hundreds of engineers across multiple disciplines. The interaction between flight and ground software development, Verification and Validation (V&V), Assembly, Test, and Launch Operations (ATLO), and management each demand quick understanding of unique slices of information for each discipline. This information includes the current capabilities of the flight system as well as future capabilities and their status as they are developed and tested. Despite the fundamental and critical nature of this information, the flight software dictionaries used to track it are a stumbling block for many projects. These dictionaries provide the cornerstone for the interpretation of data sent from the spacecraft, allowing for quick comprehension by engineers on the ground. During both spacecraft development and operations, flight software dictionary management includes significant challenges due to the large number of interfacing systems and the subtle yet distinct needs of each.The engineering of flight software dictionaries for Mars2020 had numerous challenges, most-notably: parallel dictionary development to support simultaneous separate flight software build campaigns for each mission phase (cruise and surface), managing requests for operations-enabling information without perturbing the heritage interface with the rover, and the introduction of new tools by the dictionary stakeholders that forced the dictionary team to innovate and redesign the heritage tool chain. These challenges generated guiding principles for the dictionary development effort: emphasize coding best practices and unit testing in the dictionary code development tool chain, use institutionally provided COTS (commercial-off-the-shelf) tools whenever possible, and maintain the heritage flight-ground interface all while advancing operations-enabling information via a loosely coupled interface.Throughout development and operations, the Mars2020 dictionary toolchain included IBM DOORS Next Generation, GitHub, Microsoft Excel, Docker, Jenkins, and a significant custom-built Python codebase. Significant interfaces included JPL’s command and control software, heritage flight software team tools and processes, and the many cloud-based ground tools developed for the mission.This paper will discuss the requirements for the Mars2020 dictionary development, the development team’s response to those requirements, lessons learned throughout the process, steps taken towards automated deliveries and continuous integration of stakeholder inputs, potential toolchain improvements for Mars2020, and key takeaways that could be applied to future missions.

Pyrzak, Guy↗
NASA NTRSDate not supplied

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Verification

- This presentation applies statistics to launch vehicle design, but the methods may be used for other engineering applications - Vehicle Models: What do we know when? How to separate parameters for making Monte Carlo runs - Known when assembling the vehicle but not during design - Known prior to committing to flight - Unknown at lift off - How do we correctly model the vehicle during the design phases? - Number of Monte Carlo samples - Requirements success - Design parameter values

Monte Carlo↗
NASA NTRSDate not supplied

Launch Vehicle Design and Requirements Verification Using Statistical Methods

- This presentation applies statistics to launch vehicle design, but the methods may be used for other engineering applications - Vehicle Models: What do we know when? How to separate parameters for making Monte Carlo runs - Known when assembling the vehicle but not during design - Known prior to committing to flight - Unknown at lift off - How do we correctly model the vehicle during the design phases? - Number of Monte Carlo samples - Requirements success - Design parameter values

Monte Carlo↗