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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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Space Shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - orbiter proximity data, Mach number 5
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - booster proximity data, Mach number 3
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - orbiter proximity data, Mach number 3
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - booster proximity data, Mach number 2
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - orbiter proximity data, Mach number 2
Thermal analysis data plots for heat loads of electronic packages, environmental control systems, and vehicle thermal systems
When the final Skylab mission was completed in 1974, the onboard systems were used to maneuver the vehicle to a gravity gradient equilibrium attitude and were then shut down. Parked in this storage attitude, the vehicle was left undisturbed until March 1978 when contact was reestablished and the control systems were reactivated. During the intervening four years, the vehicle was subjected to subtle environmental torques which caused the vehicle to gradually progress from its initial stable state to a very dynamic condition just prior to the resumption of control. The paper deals with the dynamics of this process, including the analyses and simulations which were made to explain the motion and enable the planning of the reactivation process. The complementary roles of both numerical integration and closed-form analysis are demonstrated. Flight data obtained in the spring of 1978 are presented which support the analytical and simulation results.
Force and moment data are presented which were obtained for each vehicle separately at a Mach number of 0.6, and for the mated orbiter/747 configuration at Mach numbers of 0.3, 0.5, 0.6, and 0.7. Orbiter angles of attack from 0 degrees to +12 degrees and 747/Carrier angles of attack from -3 degrees to +7 degrees were investigated at angles of sideslip of 0 degrees and -5 degrees. Model variables include orbiter elevon and rudder deflections, orbiter tail cone-on and off, various orbiter/747 attach structure configurations, 747 stabilizer and rudder deflections, and 747 CAM modification components-on and off. Photographs of test configurations are included.
This paper describes the current developments in video-based sensors at the Marshall Space Flight Center. The Advanced Video Guidance Sensor is the latest in a line of video-based sensors designed for use in automated docking systems. The X-33, X-34, X-38, and X-40 are all designed to be unpiloted vehicles; such vehicles will require a sensor system that will provide adequate data for the vehicle to accomplish its mission. One of the primary tasks planned for re-usable launch vehicles is to resupply the space station. In order to approach the space station in a self-guided manner, the vehicle must have a reliable and accurate sensor system to provide relative position and attitude information between the vehicle and the space station. The Advanced Video Guidance Sensor is being designed and built to meet this requirement, as well as requirements for other vehicles docking to a variety of target spacecraft. The Advanced Video Guidance Sensor is being designed to allow range and bearing information to be measured at ranges up to 2 km. The sensor will measure 6-degree-of-freedom information (relative positions and attitudes) from approximately 40 meters all the way in to final contact (approximately 1 meter range). The sensor will have a data output rate of 20 Hz during tracking mode, and will be able to acquire a target within one half of a second. The prototype of the sensor will be near completion at the time of the conference.
Techniques and support software for the efficient performance of simulation validation are discussed. Overall validation software structure, the performance of validation at various levels of simulation integration, guidelines for check case formulation, methods for real time acquisition and formatting of data from an all up operational simulator, and methods and criteria for comparison and evaluation of simulation data are included. Vehicle subsystems modules, module integration, special test requirements, and reference data formats are also described.
The general methodology for the analysis of arbitrary nonstationary random data is reviewed. A specific parametric model, called the product model, that has applications to space vehicle launch vibration data analysis is discussed. Illustrations are given using the nonstationary launch vibration data measured on the Space Shuttle orbiter vehicle.
Launch vehicle systems are designed and developed using both heritage and new hardware. Design modifications to the heritage hardware to fit new functional system requirements can impact the applicability of heritage reliability data. Risk estimates for newly designed systems must be developed from generic data sources such as commercially available reliability databases using reliability prediction methodologies, such as those addressed in MIL-HDBK-217F. Failure estimates must be converted from the generic environment to the specific operating environment of the system in which it is used. In addition, some qualification of applicability for the data source to the current system should be made. Characterizing data applicability under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This paper will demonstrate a data-source applicability classification method for suggesting epistemic component uncertainty to a target vehicle based on the source and operating environment of the originating data. The source applicability is determined using heuristic guidelines while translation of operating environments is accomplished by applying statistical methods to MIL-HDK-217F tables. The paper will provide one example for assigning environmental factors uncertainty when translating between operating environments for the microelectronic part-type components. The heuristic guidelines will be followed by uncertainty-importance routines to assess the need for more applicable data to reduce model uncertainty.
The feasibility of using range/pointing angle data such as might be obtained by a laser rangefinder for the purpose of terrain evaluation in the 10-40 meter range on which to base the guidance of an autonomous rover was investigated. The decision procedure of the rapid estimation scheme for the detection of discrete obstacles has been modified to reinforce the detection ability. With the introduction of the logarithmic scanning scheme and obstacle identification scheme, previously developed algorithms are combined to demonstrate the overall performance of the intergrated route designation system using laser rangefinder. In an attempt to cover a greater range, 30 m to 100 mm, the problem estimating gradients in the presence of positioning angle noise at middle range is investigated.
Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.
The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.
A sensitivity analysis is given of the benefits and drawbacks associated with a proposed Earth to orbit vehicle architecture. The architecture represents a fleet of six vehicles (two existing, four proposed) that would be responsible for performing various missions as mandated by NASA and the U.S. Air Force. Each vehicle has a prescribed flight rate per year for a period of 31 years. By exposing this fleet of vehicles to a probabilistic environment where the fleet experiences failures, downtimes, setbacks, etc., the analysis involves determining the resiliency and costs associated with the fleet of specific vehicle/subsystem reliabilities. The resources required were actual observed data on the failures and downtimes associated with existing vehicles, data based on engineering judgement for proposed vehicles, and the development of a sensitivity analysis program.
This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. Furthermore, these training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.
For 11 years the Department of Defense has cooperated with a diverse array of other Federal agencies including the National Institute of Standards and Technology, the Jet Propulsion Laboratory, and the Department of Energy, to develop robotics technology for unmanned ground systems. These activities have addressed control system architectures supporting sharing of tasks between the system operator and various automated subsystems, man-machine interfaces to intelligent vehicles systems, video compression supporting vehicle driving in low data rate digital communication environments, multiple simultaneous vehicle control by a single operator, path planning and retrace, and automated obstacle detection and avoidance subsystem. Performance metrics and test facilities for robotic vehicles were developed permitting objective performance assessment of a variety of operator-automated vehicle control regimes. Progress in these areas will be described in the context of robotic vehicle testbeds specifically developed for automated vehicle research. These initiatives, particularly as regards the data compression, task sharing, and automated mobility topics, also have relevance in the space environment. The intersection of technology development interests between these two communities will be discussed in this paper.