SEARCH · Engineering Papers

Results for “vehicle data”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

NASA NTRS1994

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments.

Source record↗

NASA NTRS2000

Image Discrimination Models for Object Detection in Natural Backgrounds

This paper reviews work accomplished and in progress at NASA Ames relating to visual target detection. The focus is on image discrimination models, starting with Watson's pioneering development of a simple spatial model and progressing through this model's descendents and extensions. The application of image discrimination models to target detection will be described and results reviewed for Rohaly's vehicle target data and the Search 2 data. The paper concludes with a description of work we have done to model the process by which observers learn target templates and methods for elucidating those templates.

Ahumada, A. J., Jr.↗

NASA NTRS1997

"TPSX: Thermal Protection System Expert and Material Property Database"

The Thermal Protection Branch at NASA Ames Research Center has developed a computer program for storing, organizing, and accessing information about thermal protection materials. The program, called Thermal Protection Systems Expert and Material Property Database, or TPSX, is available for the Microsoft Windows operating system. An "on-line" version is also accessible on the World Wide Web. TPSX is designed to be a high-quality source for TPS material properties presented in a convenient, easily accessible form for use by engineers and researchers in the field of high-speed vehicle design. Data can be displayed and printed in several formats. An information window displays a brief description of the material with properties at standard pressure and temperature. A spread sheet window displays complete, detailed property information. Properties which are a function of temperature and/or pressure can be displayed as graphs. In any display the data can be converted from English to SI units with the click of a button. Two material databases included with TPSX are: 1) materials used and/or developed by the Thermal Protection Branch at NASA Ames Research Center, and 2) a database compiled by NASA Johnson Space Center 9JSC). The Ames database contains over 60 advanced TPS materials including flexible blankets, rigid ceramic tiles, and ultra-high temperature ceramics. The JSC database contains over 130 insulative and structural materials. The Ames database is periodically updated and expanded as required to include newly developed materials and material property refinements.

Squire, Thomas H.↗

NASA NTRS2001

D3: A Collaborative Infrastructure for Aerospace Design

Walton, Joan↗

NASA NTRS2002

The D3 Middleware Architecture

DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid-dynamics) model executions. DARWIN captures, stores and indexes data; manages derived knowledge (such as visualizations across multiple datasets); and provides an environment for designers to collaborate in the analysis of test results. DARWIN is an interesting application because it supports high-volumes of data. integrates multiple modalities of data display (e.g., images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and views of data. Here we provide an overview of the architecture of D3, the third generation of DARWIN. Earlier versions of DARWIN were characterized by browser-based interfaces and a hodge-podge of server technologies: CGI scripts, applets, PERL, and so forth. But browsers proved difficult to control, and a proliferation of computational mechanisms proved inefficient and difficult to maintain. D3 substitutes a pure-Java approach for that medley: A Java client communicates (though RMI over HTTPS) with a Java-based application server. Code on the server accesses information from JDBC databases, distributed LDAP security services, and a collaborative information system. D3 is a three tier-architecture, but unlike 'E-commerce' applications, the data usage pattern suggests different strategies than traditional Enterprise Java Beans - we need to move volumes of related data together, considerable processing happens on the client, and the 'business logic' on the server-side is primarily data integration and collaboration. With D3, we are extending DARWIN to handle other data domains and to be a distributed system, where a single login allows a user transparent access to test results from multiple servers and authority domains.

Walton, Joan↗

NASA NTRS2004

Orbit Optimization For The Geospace Electrodynamics Connections (GEC) Mission

Part of NASA's Solar Terrestrial Probe line of missions, the Geospace Electrodynamics Connections (GEC) mission will deploy a formation of three spacecraft to perform in-situ atmospheric research in the low Ionosphere-Thermosphere region. These spacecraft will fly together in a %tring-of-pearls formation with variable spacings ranging from 10 seconds to one-quarter of an orbit at perigee. Over the course of its two-year mission, the three spacecraft will perform ten, 1-week dipping campaigns whereby they maneuver to lower their perigee to near 134 km. Using available launch vehicle performance data, an optimal parking orbit of 222 x 1525 km was found to maximize the dry mass available while providing enough propellant to perform the ten deep-dipping campaigns over its two-year mission. The results were used to create multi-variable contour plots containing the orbit perigee, the orbit apogee, spacecraft dry mass, propellant mass, and T500 (a science data collection figure of merit that tabulates the cumulative time spent below 500 km). These plots illustrate how the mission can trade off science return relative to the cost in dry mass and propellant. Other optimal solutions such as minimum propellant or maximum T500 were found to either limit the science data collection or to be dry mass limiting, respectively. Sensitivity analyses were performed to find new optimal (maximum dry mass) solutions if the number of campaigns changed, if the coefficient of drag (CD) were different, and if the propellant specific impulse were increased. A surprising result showed that the dry mass and T500 were both increased if the number of campaigns decreased. Changes in CD provided the expected results - raising CD lowered both the dry mass and T500 while lowering CD raised both the dry mass and T500. Increases in the propellant specific impulse had the expected outcome of raising the dry mass and lowering the propellant load but there was no change in the T500 figure of merit. The orbit optimization was performed parametrically using a Matlab(TradeMark) script and validated using FreeFlyer(TradeMark), a commercial orbit analysis tool. ,

Mesarch, Michael A.↗

NASA NTRS2007

Integrated Aerodynamic/Structural/Dynamic Analyses of Aircraft with Large Shape Changes

The conceptual and preliminary design processes for aircraft with large shape changes are generally difficult and time-consuming, and the processes are often customized for a specific shape change concept to streamline the vehicle design effort. Accordingly, several existing reports show excellent results of assessing a particular shape change concept or perturbations of a concept. The goal of the current effort was to develop a multidisciplinary analysis tool and process that would enable an aircraft designer to assess several very different morphing concepts early in the design phase and yet obtain second-order performance results so that design decisions can be made with better confidence. The approach uses an efficient parametric model formulation that allows automatic model generation for systems undergoing radical shape changes as a function of aerodynamic parameters, geometry parameters, and shape change parameters. In contrast to other more self-contained approaches, the approach utilizes off-the-shelf analysis modules to reduce development time and to make it accessible to many users. Because the analysis is loosely coupled, discipline modules like a multibody code can be easily swapped for other modules with similar capabilities. One of the advantages of this loosely coupled system is the ability to use the medium-to high-fidelity tools early in the design stages when the information can significantly influence and improve overall vehicle design. Data transfer among the analysis modules are based on an accurate and automated general purpose data transfer tool. In general, setup time for the integrated system presented in this paper is 2-4 days for simple shape change concepts and 1-2 weeks for more mechanically complicated concepts. Some of the key elements briefly described in the paper include parametric model development, aerodynamic database generation, multibody analysis, and the required software modules as well as examples for a telescoping wing, a folding wing, and a bat-like wing.

Samareh, Jamshid A.↗

NASA NTRS1989

Acquisition signal transmitter

An encoded information transmitter which transmits a radio frequency carrier that is amplitude modulated by a constant frequency waveform and thereafter amplitude modulated by a predetermined encoded waveform, the constant frequency waveform modulated carrier constituting an acquisition signal and the encoded waveform modulated carrier constituting an information bearing signal, the acquisition signal providing enhanced signal acquisition and interference rejection favoring the information bearing signal. One specific application for this transmitter is as a distress transmitter where a conventional, legislated audio tone modulated signal is transmitted followed first by the acquisition signal and then the information bearing signal, the information bearing signal being encoded with, among other things, vehicle identification data. The acquistion signal enables a receiver to acquire the information bearing signal where the received signal is low and/or where the received signal has a low signal-to-noise ratio in an environment where there are multiple signals in the same frequency band as the information bearing signal.

Friedman, Morton L.↗

NASA NTRS2009

Efficient Multidisciplinary Analysis Approach for Conceptual Design of Aircraft with Large Shape Change

The conceptual and preliminary design processes for aircraft with large shape changes are generally difficult and time-consuming, and the processes are often customized for a specific shape change concept to streamline the vehicle design effort. Accordingly, several existing reports show excellent results of assessing a particular shape change concept or perturbations of a concept. The goal of the current effort was to develop a multidisciplinary analysis tool and process that would enable an aircraft designer to assess several very different morphing concepts early in the design phase and yet obtain second-order performance results so that design decisions can be made with better confidence. The approach uses an efficient parametric model formulation that allows automatic model generation for systems undergoing radical shape changes as a function of aerodynamic parameters, geometry parameters, and shape change parameters. In contrast to other more self-contained approaches, the approach utilizes off-the-shelf analysis modules to reduce development time and to make it accessible to many users. Because the analysis is loosely coupled, discipline modules like a multibody code can be easily swapped for other modules with similar capabilities. One of the advantages of this loosely coupled system is the ability to use the medium- to high-fidelity tools early in the design stages when the information can significantly influence and improve overall vehicle design. Data transfer among the analysis modules are based on an accurate and automated general purpose data transfer tool. In general, setup time for the integrated system presented in this paper is 2-4 days for simple shape change concepts and 1-2 weeks for more mechanically complicated concepts. Some of the key elements briefly described in the paper include parametric model development, aerodynamic database generation, multibody analysis, and the required software modules as well as examples for a telescoping wing, a folding wing, and a bat-like wing. The paper also includes the verification of a medium-fidelity aerodynamic tool used for the aerodynamic database generation with a steady and unsteady high-fidelity CFD analysis tool for a folding wing example.

Chwalowski, Pawel↗

NASA NTRS2009

Investigating Response from Turbulent Boundary Layer Excitations on a Real Launch Vehicle using SEA

Statistical Energy Analysis (SEA) response has been fairly well anchored to test observations for Diffuse Acoustic Field (DAF) loading by others. Meanwhile, not many examples can be found in the literature anchoring the SEA vehicle panel response results to Turbulent Boundary Layer (TBL) fluctuating pressure excitations. This deficiency is especially true for supersonic trajectories such as those required by this nation s launch vehicles. Space Shuttle response and excitation data recorded from vehicle flight measurements during the development flights were used in a trial to assess the capability of the SEA tool to predict similar responses. Various known/measured inputs were used. These were supplemented with a range of assumed values in order to cover unknown parameters of the flight. This comparison is presented as "Part A" of the study. A secondary, but perhaps more important, objective is to provide more clarity concerning the accuracy and conservatism that can be expected from response estimates of TBL-excited vehicle models in SEA (Part B). What range of parameters must be included in such an analysis in order to land on the conservative side in response predictions? What is the sensitivity of changes in these input parameters on the results? The TBL fluid structure loading model used for this study is provided by the SEA module of the commercial code VA One.

Harrison, Phillip↗

NASA NTRS2009

Analysis of Space Shuttle Ground Support System Fault Detection, Isolation, and Recovery Processes and Resources

As part of the FDIR (Fault Detection, Isolation, and Recovery) Project for the Constellation Program, a task was designed within the context of the Constellation Program FDIR project called the Legacy Benchmarking Task to document as accurately as possible the FDIR processes and resources that were used by the Space Shuttle ground support equipment (GSE) during the Shuttle flight program. These results served as a comparison with results obtained from the new FDIR capability. The task team assessed Shuttle and EELV (Evolved Expendable Launch Vehicle) historical data for GSE-related launch delays to identify expected benefits and impact. This analysis included a study of complex fault isolation situations that required a lengthy troubleshooting process. Specifically, four elements of that system were considered: LH2 (liquid hydrogen), LO2 (liquid oxygen), hydraulic test, and ground special power.

Gross, Anthony R.↗

NASA NTRS2011

A Compilation of Space Shuttle Sonic Boom Measurements

Sonic boom measurements have been obtained on 26 flights of the Space Shuttle system beginning with the launch of STS-1 on April 12, 1981, to the reentry-descent of STS-41 into EAFB on Oct. 10, 1990. A total of 23 boom measurements were acquired within the focus region off the Florida coast during 3 STS launch-ascents and 113 boom measurements were acquired during 23 STS reentry-descent to landing into Florida and California. Sonic boom measurements were made under, and lateral to, the vehicle ground track and cover the Mach-altitude range of about 1.3 to 23 and 54,000 feet to 243,000 feet, respectively. Vehicle operational data, flight profiles and weather data were also gathered during the flights. This STS boom database is contained in 26 documents, some are formal and referenceable but most internal documents. Another 38 documents, also non-referenceable, contain predicted sonic boom footprints for reentry-descent flights on which no measurements were made. The purpose of this report is to provide an overview of the STS sonic boom database and summarize the main findings.

Maglieri, Domenic J.↗

NASA NTRS2017

An N+3 Technology Level Reference Propulsion System

An N+3 technology level engine, suitable as a propulsion system for an advanced single-aisle transport, was developed as a reference cycle for use in technology assessment and decision-making efforts. This reference engine serves three main purposes: it provides thermodynamic quantities at each major engine station, it provides overall propulsion system performance data for vehicle designers to use in their analyses, and it can be used for comparison against other proposed N+3 technology-level propulsion systems on an equal basis. This reference cycle is meant to represent the expected capability of gas turbine engines in the N+3 timeframe given reasonable extrapolations of technology improvements and the ability to take full advantage of those improvements.

Jones, Scott M.↗

NASA NTRS2017

MIL-HDBK-338: Environmental Conversion Table Correction

In reliability analysis, especially for launch vehicles, limited data is frequently a problem. Component data from other environments must be used. MIL-HBK-338 has a matrix showing the conversation between environments. Due to round off the conversions are not commutative, converting from A to B will not equal converting from B to A. Agenda: Introduction to environment conversions; Original table; Original table with edits; How big is the problem?; First attempt at correction; Proposed solution.

Hark, Frank↗

NASA NTRS2017

MIL-HDBK-338-Environmental Conversion Table Correction

In reliability analysis for space launch vehicles, limited data is frequently a challenge due to the pure number of launches. A common solution is to use surrogate historical data of similar components from other industries (military data). The operating environment of the common data may be different from that of the necessary target analysis. The military electronic design handbook (MIL-HDBK-338) has a table for converting Mean Time Between Failure (MTBF) data from one environment to another. However, the table has some discrepancies and rounding of complementary conversions; namely going from environment A to B does not given the same result as going from B to A. This presentation will show the discrepancies in the original conversation table, the greater than expected magnitude, the problem with the updated published table and a suggested corrected table to reference when doing MTBF data environment conversion.

Hark, Frank↗

NASA NTRSDate not supplied

Aftbody Aeroheating Wind Tunnel Measurements on a Hypersonic Inflatable Aerodynamic Decelerator

The aftbody aeroheating environment of a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) was investigated in the NASA Langley 31-Inch Mach 10 Air Tunnel. The wind tunnel model geometry was representative of the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) vehicle. Aeroheating data were obtained for a range of Reynolds numbers and angles-of-attack using global phosphor thermography. Aeroheating data were also obtained using a developmental IR thermography technique for evaluation of data quality.

Elizabeth F Rieken↗

NASA NTRSDate not supplied

SIRIUS: Simulation Infrastructure for Research on Interoperating Unmanned Systems

This paper presents the Simulation Infrastructure for Research on Interoperating Unmanned Systems (SIRIUS), a research framework for simulation and analysis of future conceptual Urban Air Mobility (UAM) operations. SIRIUS is being developed under the auspices of the NASA Air Traffic Management eXploration project, UAM subproject (ATM-X UAM). SIRIUS provides an intuitive, highly configurable graphical user interface to design complex traffic scenarios and airspace configurations representative of conceptual UAM operations. Aircraft simulated with SIRIUS can be equipped with flight-tested capabilities for detect and avoid (DAA), geofencing, distributed merging and spacing, path conformance, and path planning while executing time-constrained, 4D trajectories generated by a UAM ground operations system. Central to the design of the SIRIUS simulation framework is the capability to evaluate the integration and interoperability of ground-based separation services (e.g., strategic separation) with extended DAA functionality (e.g., path monitoring, separation provision, merging and spacing, etc.) The simulation environment also supports modelling of wind, navigation, and sensor uncertainties, as well as communication delays. SIRIUS enables distributed simulation of large-scale scenarios. An interactive graphical analysis capability helps isolate, visualize, and compare relevant vehicle state data and widely used measures of performance metrics across multiple scenarios.

Andrew Peters↗

NASA NTRSDate not supplied

Trajectory Reconstruction of the Low-Earth Orbit Flight Test of an Inflatable Decelerator

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) project conducted a flight test of a 6m inflatable aeroshell. The LOFTID test article was a secondary payload on an Atlas V launcher that carried the Joint Polar Satellite System-2 (JPSS-2) as its primary payload. The vehicle launched on November 10th, 2022. After reaching orbit, the LOFTID test article inflated the aeroshell, separated from the upper stage on an entry trajectory, and entered the atmosphere to splash down in the Pacific Ocean under parachutes. The test concept of operations is shown in Figure 1. The test article was instrumented with a variety of sensors to be used for post-flight evaluation of vehicle performance. Data from one of the key sensors for trajectory reconstruction, the Inertial Measurement Unit (IMU), was not captured in the data recorder due to a malfunction. Data from the nose cone mounted Flush Air Data Sensing (FADS) system were successfully acquired. The layout of the FADS sensors and the measured pressures during atmospheric entry are shown in Figure 2. The FADS data were combined with a Newtonian flow pressure model [1, 2] to produce estimates of the atmospheric relative trajectory. A Mach number anchoring technique given in [2] was used to stabilize estimates in high speed flight conditions. Since no IMU data were available, a trajectory simulation was used to provide the Mach number time history. The resulting estimates of the atmospheric-relative trajectory are shown in Figures 3. Given the loss of the IMU data, alternate methods for trajectory reconstruction are being explored. One approach under investigation is the use of the on-board video recorder data to be analyzed to reconstruct attitude motion. This approach is currently under investigation and will be reported on in the final paper. The Newtonian flow pressure model for the FADS analysis will also be updated with a CFD-based pressure model.

Christopher D Karlgaard↗