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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 NTRS2018

Vehicle Design Data Format and Process for a Complete TARIS and OLTARIS Radiation Analysis for Designers and Engineers

Protecting astronauts from space radiation is a complex task when it comes to modeling and simulation. This document shows what information is needed from a spacecraft designer using CAD (Computer-Assisted Design) at each phase of the design to enable the engineers to evaluate the design phase against space radiation limits to determine the suitability of the design for space flight. The current personal exposure limits are listed in NASA STD-3001. A proxy to determine the REID (Radiation Exposure Induced Death) in NASA STD-3001 is the whole body effective dose equivalent (E or effective dose). For short-term tissue effects, organ-averaged gray equivalent (G (sub T)) is used. The TARIS (Tool for the Assessment of Radiation In Space - for LaRC (Langley Research Center) engineers) and OLTARIS (On-Line TARIS) - for designers) systems are used to generate these response functions. The E can use ICRP60 or NASA Q-values. A possible space radiation design basis environment for short-term tissue effects is described and used in all analyses. A single space vehicle was designed with three astronaut configurations and two of those configurations were used in a storm shelter thickness perturbation analysis. Conversion of the data from the CAD model to input necessary for TARIS and OLTARIS is also discussed in detail with relevant examples.

Singleterry, Robert C.↗
NASA NTRS1989

Application of statistical distribution theory to launch-on-time for space construction logistic support

The ability to launch-on-time and to send payloads into space has progressed dramatically since the days of the earliest missile and space programs. Causes for delay during launch, i.e., unplanned 'holds', are attributable to several sources: weather, range activities, vehicle conditions, human performance, etc. Recent developments in space program, particularly the need for highly reliable logistic support of space construction and the subsequent planned operation of space stations, large unmanned space structures, lunar and Mars bases, and the necessity of providing 'guaranteed' commercial launches have placed increased emphasis on understanding and mastering every aspect of launch vehicle operations. The Center of Space Construction has acquired historical launch vehicle data and is applying these data to the analysis of space launch vehicle logistic support of space construction. This analysis will include development of a better understanding of launch-on-time capability and simulation of required support systems for vehicle assembly and launch which are necessary to support national space program construction schedules. In this paper, the author presents actual launch data on unscheduled 'hold' distributions of various launch vehicles. The data have been supplied by industrial associate companies of the Center for Space Construction. The paper seeks to determine suitable probability models which describe these historical data and that can be used for several purposes such as: inputs to broader simulations of launch vehicle logistic space construction support processes and the determination of which launch operations sources cause the majority of the unscheduled 'holds', and hence to suggest changes which might improve launch-on-time. In particular, the paper investigates the ability of a compound distribution probability model to fit actual data, versus alternative models, and recommends the most productive avenues for future statistical work.

Morgenthaler, George W.↗
DOE OSTI2023

2022 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2022 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2022 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as buses, 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "ATB Transportation Website" resource below for more project information.

2022↗
DOE OSTI2024

2024 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2024 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "Transportation ATB Website" resource below for more project information.

2024↗
NASA NTRS2002

Closed-Loop Endoatmospheric Ascent Guidance

This paper will present a complete formulation of the optimal control problem for atmospheric ascent of rocket powered launch vehicles subject to usual load constraints and final condition constraints. We shall demonstrate that the classical finite difference method for two-point-boundary-value-problems (TPBVP) is suited for solving the ascent trajectory optimization problem in real time, therefore closed-loop optimal endoatmospheric ascent guidance becomes feasible. Numerical simulations with a the vehicle data of a reusable launch vehicle will be provided.

Lu, Ping↗
NASA NTRS1980

A high speed telemetry data link for an autonomous roving vehicle

A data link system used on a prototype autonomous roving vehicle is described. This system provides a means of acquiring, formatting, and transmitting information on board the vehicle to a controlling computer. Included is a statement of requirements and the design philosophy. Additionally, interfacing with the rover systems is discussed, along with the overall performance of the telemetry link.

Cipolle, D. J.↗
NASA NTRS2010

Photogrammetric Measurements of an EH-60L Brownout Cloud

There is a critical lack of quantitative data regarding the mechanism of brownout cloud formation. Recognizing this, tests were conducted during the Air Force Research Lab 3D-LZ Brownout Test at the US Army Yuma Proving Ground. Photogrammetry was utilized during two rounds of flight tests with an instrumented EH-60L Black Hawk to determine if this technique could quantitatively measure the formation and evolution of a brownout cloud. Specific areas of interest include the location, size, and average convective velocity of the cloud, along with the characteristics of any defined structures within it. Following the first flight test, photogrammetric data were validated through comparison with onboard vehicle data. Lessons learned from this test were applied to the development of an improved photogrammetry system. A second flight test, utilizing the improved system, demonstrated that obtaining quantitative measurements of the brownout cloud are possible. Results from these measurements are presented in the paper. Flow visualization with chalk dust seeding was also tested. It was observed that pickup forces of the brownout cloud appear to be very low. Overall, these tests demonstrate the viability of photogrammetry as a means for quantifying brownout cloud formation and evolution.

Wong, Oliver D.↗
NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗
NASA NTRS1992

Shuttle entry aerothermodynamic flight research - The Orbiter Experiments (OEX) Program

Results of the OEX program are summarized with emphasis on the information on entry aerothermodynamic phenomena derived from Space Shuttle operations. The discussion focuses on OEX experiment complement and operational history, freestream environment and vehicle attitude data, aerodynamic force and moment data, aerodynamic surface data, and vehicle configuration data. Attention is also given to orbiter aerodynamic performance, stability and control, high-altitude atmospheric density variability, direct simulation Monte Carlo validation, orbital drag variation, and computational fluid dynamic technique validation.

Throckmorton, David A.↗
NASA NTRS1967

Project - Lunar orbiter D

NASA information release of Lunar Orbiter D launching, including configurations, systems, mission, and Atlas Agena launch vehicle data

SATELLITE LAUNCHING↗