ALBERT

Description: This viewgraph presentation gives a general overview of the X-43A program. The contents include: 1) X-43A Program Overview; 2) Vehicle Description; 3) Flight 1, MIB & Return to Flight; 4) Flight 2 and Results; and 5) Flight 3 and Results.

Description: A viewgraph presentation describing the hypersonics program at NASA Dryden Flight Research Center is shown. The topics include: 1) X-43A Program Overview; 2) Vehicle Description; 3) Flight 1, MIB & Return to Flight; 4) Flight 2 and Results; 5) Flight 3 and Results; and 6) Concluding Remarks

Description: Historically, our ability to predict and postdict surface charging has suffered from both a lack of reliable secondary emission and backscattered electron yields and poor characterization of the plasma environment. One difficulty lies in the common practice of fitting the plasma data to a Maxwellian or Double Maxwellian distribution function, which may not represent the data well for charging purposes. For 13 years Los Alamos National Laboratory (LANL) has been accumulating measurements of electron and proton spectra from Magnetospheric Plasma Analyzer (MPA) instruments aboard a series of geosynchronous satellites. These data provide both a plasma characterization and the potential of the instrument ground. We use electron and ion flux spectra measured by the LANL MPA to examine how the use of different spectral representations of the charged particle environment in computations of spacecraft potentials during magnetospheric substorms affects the accuracy of the results. We calculate the spacecraft potential using both the measured fluxes and several different fits to these fluxes. These flux measurements and fits have been corrected for the difference between the measured and calculated potential. The potentials computed using the measured fluxes, the best available material properties of graphite carbon, and a secondary electron escape fraction of 81%, are within a factor of three of the measured potential for nearly all the data. Using a Kappa fit to the electron distribution function and a Maxwellian fit to the ion distribution function gives agreement similar to the calculations using the actual data. Alternative spectral representations, including Maxwellian and double Maxwellian for both species, lead to less satisfactory agreement between predicted and measured potentials.

Description: Nascap-2k is the modern replacement for the older 3-D charging codes NASCAP/GEO, NASCAP/LEO, POLAR, and DynaPAC. Built on the DynaPAC kernel and incorporating surface charging, environment and space potential models from the older codes, Nascap-2k performs charging calculations for a wide variety of space environments under control of a unified graphical interface. In this paper we illustrate the use of Nascap-2k for spacecraft charging calculations. We touch on some of the unique physical and mathematical models on which the code is based. Examples/demos include the use of Object Toolkit, charging calculations in geosynchronous substorm, solar wind, low earth orbit, and auroral environments, and display and analysis of surface potentials, space potentials and particle trajectories.

Description: In less than two years, the National Aeronautics and Space Administration (NASA) will launch the Ares I-X mission. This will be the first flight of the Ares I crew launch vehicle, which, together with the Ares V cargo launch vehicle, will send humans to the Moon and beyond. Personnel from the Ares I-X Mission Management Office (MMO) are finalizing designs and fabricating vehicle hardware for an April 2009 launch. Ares I-X will be a suborbital development flight test that will gather critical data about the flight dynamics of the integrated launch vehicle stack; understand how to control its roll during flight; better characterize the severe stage separation environments that the upper stage engine will experience during future flights; and demonstrate the first stage recovery system. NASA also will modify the launch infrastructure and ground and mission operations. The Ares I-X Flight Test Vehicle (FTV) will incorporate flight and mockup hardware similar in mass and weight to the operational vehicle. It will be powered by a four-segment Solid Rocket Booster (SRB), which is currently in Shuttle inventory, and will include a fifth spacer segment and new forward structures to make the booster approximately the same size and weight as the five-segment SRB. The Ares I-X flight profile will closely approximate the flight conditions that the Ares I will experience through Mach 4.5, up to approximately130,OOO feet and through maximum dynamic pressure ("Max Q") of approximately 800 pounds per square foot. Data from the Ares I-X flight will support the Ares I Critical Design Review (CDR), scheduled for 2010. Work continues on Ares I-X design and hardware fabrication. All of the individual elements are undergoing CDRs, followed by an integrated vehicle CDR in March 2008. The various hardware elements are on schedule to begin deliveries to Kennedy Space Center (KSC) in early September 2008.

Description: The NASA Charging Analyzer Program (NASCAP) spacecraft charging software developed by Maxwell Technologies has been widely used for the past fifteen to twenty years in satellite design and investigation of spacecraft charging related anomalies. Individual versions of the NASCAP software are available for use in low inclination, low Earth orbit environments (NASCAP[LEO) and geostationary orbit environments (NASCAP/GEO). In addition, the Potentials of Large objects in the Auroral Region (POLAR) code is available for use in LEO polar orbit environments. NASCAP/GEO and POLAR were both written in the 1980's using algorithms appropriate for the computers of the time. They solve the Poisson-Vlasov system for currents and densities assuming limited speed and memory of computer systems standard for the day. In addition, use of the charging models requires individual input files that are not readily transported into the various codes to facilitate comparison of results by the user.

Description: The Microwave Anisotropy Probe (MAP) was launched June 30, 2001 to create an all-sky map of the Cosmic Microwave Background. The mission's hardware suite included two Lockheed Martin AST-201 star trackers, two Kearfott Two-Axis Rate Assemblies (TARAs) mounted to provide X, Y and redundant Z-axis rates, two Adcole Digital Sun Sensor (DSS) heads sharing one set of electronics, twelve Adcole Coarse Sun Sensor (CSS) eyes, three Ithaco E-sized Reaction Wheel Assemblies (RWAs), and a Propulsion Subsystem that employed eight PRIMEX Rocket Engine Modules (REMs). This hardware has allowed MAP to meet its various Orbit and Attitude Control Requirements, including performing a complex zero-momentum scan, meeting its attitude determination requirements, and maintaining a trajectory that places MAP in a lissajous orbit around the second Sun-Earth Lagrange point (L2) via phasing loops and a lunar gravity assist. Details of MAP's attitude determination, attitude control, and trajectory design are presented separately. This paper will focus on the performance of the hardware components mentioned above, as well as the significant lessons learned through the use of these components. An emphasis will be placed on spacecraft design modifications that were needed to accommodate existing hardware designs into the MAP Observatory design.

Description: No abstract available

Description: Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Description: This paper details a novel scheme for autonomous component health management (ACHM) with failed actuator detection and failed sensor detection, identification, and avoidance. This new scheme has features that far exceed the performance of systems with triple-redundant sensing and voting, yet requires fewer sensors and could be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided, and the simulation results for the application of that scheme to a single-axis spacecraft attitude control system with a 3rd order plant and dual-redundant measurement of system states are presented. ACHM fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is: autonomous; adaptive; works in realtime; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures; reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open- and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary state estimates, detects and identifies failed sensors and actuators, and avoids the use of failed sensors in state estimation with a fixed-gain Kalman filter that generates optimal state estimates and provides model-based state estimates that comprise an integral part of the failure detection logic. The results show that ACHM successfully isolates multiple persistent and intermittent hard-over, soft, and zero-output failures. It is now ready to be tested on a computer model of an actual system.