Apollo 14 /AS-509/ operational trajectory for 31 January 1971, launch window
Predicted launch vehicle operational trajectory and related data for Apollo 14 launch window
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Predicted launch vehicle operational trajectory and related data for Apollo 14 launch window
Shuttle orbiter vehicle flight data obtained during operation of the reaction control subsystem (RCS) were compared with predictions derived from RCS wind tunnel test data. This paper reviews the derivation of the wind tunnel data base and discusses how it was used to predict the full-scale RCS effects. Flight and predicted data comparisons include the lateral and directional aerodynamic effects of firing the side jets, longitudinal aerodynamics for pitch jets, and lateral aerodynamics for roll jets. Flight data anomalies resulting from wind tunnel limitations in representing portions of the entry flight trajectory are presented. The cause of each data anomaly is described, as well as a requirement for additional technical analysis to establish RCS effect simulation parameters that can be used to update jet effect technology.
The Second International Symposium featured 135 oral presentations in these 12 categories: Future Missions and Operations; System-Level Architectures; Mission-Specific Systems; Mission and Science Planning and Sequencing; Mission Control; Operations Automation and Emerging Technologies; Data Acquisition; Navigation; Operations Support Services; Engineering Data Analysis of Space Vehicle and Ground Systems; Telemetry Processing, Mission Data Management, and Data Archiving; and Operations Management. Topics focused on improvements in the productivity, effectiveness, efficiency, and quality of mission operations, ground systems, and data acquisition. Also emphasized were accomplishments in management of human factors; use of information systems to improve data retrieval, reporting, and archiving; design and implementation of logistics support for mission operations; and the use of telescience and teleoperations.
Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.
Report characterizes state-of-the-art electric and hybrid (combined electric and heat engine) vehicles. Performance data for representative number of these vehicles were obtained from track and dynamometer tests. User experience information was obtained from fleet operators and individual owners of electric vehicles. Data on performance and physical characteristics of large number of vehicles were obtained from manufacturers and available literature.
Trajectory data are presented for a nominal and two launch window trajectory simulations. These trajectories are designed to insert a manned Apollo spacecraft into a 150/167 km. (81/90 n. mi.) earth orbit inclined at 51.78 degrees for rendezvous with a Soyuz spacecraft, which will be orbiting at approximately 225 km. (121.5 n. mi.). The launch window allocation defined for this launch is 500 pounds of S-IVB stage propellant. The launch window opening trajectory simulation depicts the earliest launch time deviation from a planar flight launch which conforms to this constraint. The launch window closing trajectory simulation was developed for the more stringent Air Force Eastern Test Range (AFETR) flight azimuth restriction of 37.4 degrees east-of-north. These trajectories enclose a 12.09 minute launch window, pertinent features of which are provided in a tabulation. Planar flight data are included for mid-window reference.
This is the research performance final report for the project entitled: Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption This project was able to achieve the DOE’s goals of developing new modeling tools to understand MDHD vehicle operation and adoption. The first modeling tool is a fleet-level techno-economic analysis model capable of estimating energy use and associated environmental and cost impacts for electrified and conventional vehicles of any MDHD vocation, using real-world cost and operations data, including approaches to optimizing schedules for charging and/or vehicle dispatch. The second modeling tool is a system-level, bottom-up, agent-based adoption model capable of generating geographically-resolved estimates of market projections for MDHD vehicles and charging infrastructure. These tools will be developed and published to serve dual purposes as analysis tools for researchers, and decision-support tools for decision makers within the MDHD system.
The Atmosphere Explorer-D (Explorer-54) is described which will explore in detail an area of the earth's outer atmosphere where important energy transfer, atomic and molecular processes, and chemical reactions occur that are critical to the heat balance of the atmosphere. Data are presented on the mission facts, launch vehicle operations, AE-D/Delta flight events, spacecraft description, scientific instruments, tracking, and data acquisition.
Mission, launch operations, communications, and test operation are discussed for the Intelsat 4 satellite. These include mission objective and plan, launch vehicle and spacecraft descriptions, postlaunch operations, data acquisition, meteorological plan, and F-1 and F-0 day operations.
As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.
This paper describes the methodology, model, input data, and analysis results of a reusable launch vehicle engine operability study conducted with the goal of supporting design from an operations perspective. Paralleling performance analyses in schedule and method, this requires the use of metrics in a validated operations model useful for design, sensitivity, and trade studies. Operations analysis in this view is one of several design functions. An operations concept was developed given an engine concept and the predicted operations and maintenance processes incorporated into simulation models. Historical operations data at a level of detail suitable to model objectives were collected, analyzed, and formatted for use with the models, the simulations were run, and results collected and presented. The input data used included scheduled and unscheduled timeline and resource information collected into a Space Transportation System (STS) Space Shuttle Main Engine (SSME) historical launch operations database. Results reflect upon the importance not only of reliable hardware but upon operations and corrective maintenance process improvements.
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.
Measured vibration data are presented for a number of air and surface vehicles. Consideration is given to the importance of direction effects; of vehicle operating modes such as takeoff, cruise, and landing; and of measurement location on the level and frequency of the measurements. Various physical measurement units or descriptors are used to quantify and compare the data. Results suggest the range of vibration associated with a particular mode of transportation and illustrate the comparative levels in terms of each of the descriptors. Collectively, the results form a data base which may be useful in assessing the ride of existing or future systems relative to vehicles in current operation. In addition, subjective response data obtained from vibration simulator studies are presented to illustrate human response characteristics as well as to indicate a laboratory approach for the development of ride-quality criteria.
Adopting advanced vehicle technologies, such as battery-electric, hybrid, and hydrogen fuel cell vehicles, can be an effective strategy for reducing fleet owners' operating costs. However, different trucking sectors, such as private and for-hire carriers and short- or long-haul operations, may face unique challenges in adopting those vehicle technologies due to their own operational needs and budget constraints. Current studies on fleet-wide vehicle technology projections frequently overlook such sectoral differences and fail to capture variation in adoption potential across sectors. This study addresses this gap by analyzing the disparities in the total cost of ownership (TCO) and payback period (PBP) among a large and heterogeneous sample of fleet owners. It aims to understand the sectoral differences in the long-term potential for adopting advanced vehicle technologies. Utilizing the 2021 US Vehicle Inventory and Use Survey (US VIUS), which offers data on various commercial vehicle sectors, their operational patterns, and current vehicle assets, this research estimates the TCO and PBP for individual trucks over multiple future years. The results reveal variation in the cost-effectiveness of different vehicle technologies across trucking sectors, as well as the potential technology landscape in both the short and long term. The findings from this study can inform policymakers and practitioners on how to prioritize sectors with lower barriers for advanced vehicle technology adoption and support industries that face challenges in switching to advanced vehicles.
After listing the basic requirements such as operational demands and lunar environmental conditions, characteristic design data of the roving vehicle are given with special attention to the four wheel drive and its control system, the steering system, and the navigational instrumentation. The overall system is optimized in view of total mass, range, mobility, load carrying capability, safety, highest reliability through redundancy, and last but not least, cost and development time. Optimization of the drive and power system has first been evaluated by mathematical models and computational simulation of the vehicle hardware, the wheel/soil interaction, and the lunar terrain profile. The next step to refine optimization and evaluation of the drive, steering, and power system including man/machine interaction has been carried out by the use of a six-degree-of-freedom simulator subjecting the astronauts in their space suits to simulated vehicle motions while driving over a simulated terrain, observable on a cathode ray tube display.
As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. Data-driven techniques have a number of advantages over other methods for monitoring complex space vehicles. Unlike model-based systems, the developer does not need to understand or encode the internal operation of the system. The knowledge required to monitor the system is automatically derived from archived data from system operation. Unlike rule-based systems, data-driven systems do not require system analysts to define nominal relationships among sensors. Analysts can and often do determine these relationships for a system with few sensors; it is more difficult to analytically determine the nominal relationship among a large number of sensors. Data-driven techniques are not limited to low-dimensional spaces and work as effectively with dozens of parameters as they do with a few. Knowledge bases formed by data-driven techniques are also easy to update. As the operating envelope of the monitored system is expanded, data-driven techniques can be quickly retrained to incorporate the new behavior into the knowledge base. The expertise and time-consuming process of updating a model or rule base to maintain consistency with the new operation is not required. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or analysis of archived events. System data is compared with the nominal IMS model to produce a measure of how well current system behavior matches the normal behavior defined by the training data. Significant deviations from the nominal system model can provide alerts to system malfunctions or precursors of significant failures. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments. Initially, the TVSM software will run open loop, providing system health information and recommendations to ground operators, without automatically performing fault-mitigating corrective actions. After the end of the satellite's mission, closed loop tests combining TVSM monitoring and diagnosis with reactive capabilities by the flight software will be performed. In addition to monitoring for long periods of actual operation, the experiment will include fault injection into TacSat-3 data as well as commanded operations to test and evaluate automatic ISHM monitoring and recovery under controlled conditions.
The news release describes the 1971 launches of Mariner 8 and 9 which were to be the first attempt by NASA to orbit another planet, Mars. Described are: (1) mission capsule; (2) planetary missions; (3) aiming zones; (4) the spacecraft; (5) scientific experiments to be performed; (6) Atlas Centaur launch vehicle; (7) launch operations; (8) tracking and data system and mission operations; and (9) Mariner Mars 71 team and subcontractors.
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.