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A summary of the actions and problems involved in preparing the Skylab-one vehicle is presented. The subjects discussed are: (1) flight operations support functions and organization, (2) launch operations and booster flight support functions and organization, (3) Skylab launch vehicle support teams, (4) Skylab orbital operations support performance analysis, (5) support manning and procedures, and (6) data support and facilities.
The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), responsible for project management and flight operations; Orbital Sciences Corporation (OSC), spacecraft builder and responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), responsible for science planning and operations. As a cost-capped mission, one of Dawn s implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL s ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL s GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project s commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to an overall systems engineering process and fundamental systems engineering practices: decomposition of the project request into manageable requirements; definition of a structured yet flexible development process; integration of multiple ground disciplines and experts into a focused team effort; in-process risk management; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.
Trace chemical contaminants produced by equipment offgassing and human metabolic processes are removed from the atmosphere of the International Space Station s U.S. Segment by a trace contaminant control subassembly (TCCS). The TCCS employs a combination of physical adsorption, thermal catalytic oxidation, and chemical adsorption processes to accomplish its task. A large bed of granular activated charcoal is a primary component of the TCCS. The charcoal contained in this bed, known as the charcoal bed assembly (CBA), is expendable and must be replaced periodically. Pre-flight engineering analyses based upon TCCS performance testing results established a service life estimate of 1 year. After nearly 1 year of cumulative in-flight operations, the first CBA was returned for refurbishment. Charcoal samples were collected and analyzed for loading to determine the best estimate for the CBAs service life. A history of in-flight TCCS operations is presented as well as a discussion of the charcoal sampling procedures and chemical analysis results. A projected service life derived from the observed charcoal loading is provided. Recommendations for better managing TCCS resources are presented.
The Cockpit Interface Control Document defines the hardware interfaces between the X-57 cockpit and subsystems. It provides locational and operational information in support of ground and flight operations with details on controls and displays that include Modes of Operation, Start-Up and Shut- Down Sequence diagrams and captures the current state of the MOD II Avionics Power Architecture. There is also preliminary information of the MOD III and MOD IV configurations. Microsoft PowerPoint was chosen for the document as early development required frequent meetings with multiple customers including aircraft operators (pilots), ground operations, support contractors and power, instrumentation, and human systems integration engineers and PowerPoint enabled presentations that could be quickly modified based on customer and developer interaction. One of the driving requirements for the cockpit design was to keep the left side panel as close the stock Tecnam panel as possible to reduce the failure risk of flight critical indicators. The original annunciator panel in the left side panel was modified to alert the pilot to failures in critical X-57 subsystems and an operator audio alert capability was added for these subsystems. Power-Up switches for the aircraft low voltage 13.8 VDC systems are located at the bottom of the left side panel and center panel, the same location as the stock Tecnam 13.8 VDC switches. The switches for energizing the high voltage system were located in the overhead panel to reduce the risk of inadvertently energizing the high voltage system during the low voltage power-up sequence. The Cruise Motor ARM switches were also located in the overhead panel and correspond to the same location as the stock Tecnam ignition switches. The stock Tecnam throttle levers and prop pitch levers were retained for the X-57. The throttle levers were renamed torque levers since they controlled the commanded torque to the cruise motors. The prop pitch levers provide a commanded RPM signal to an electronic prop pitch controller. X-57 specific displays, located in the right-side panel, are driven by dedicated sensors that monitor right and left side cruise motor RPM, right and left high voltage “Traction Bus” A and B (voltage, current and power) and the Avionics Bus DC converters (A and B) voltage and current. An X-57 Multi-Function Display (MFD) located in the center panel displays CAN Bus parameters. CAN Bus architecture is not certified for flight so these displays could not be used for safety critical information but were designed to be used for test point information only.
A significant problem associated with fault tolerant computer system design is how to insure that there are no embedded software errors, so that an avionics computer system meets the required reliability level. To accomplish this, it is necessary to associate a 'probability of failure' with the operational flight program. It would be more correct to say that the probability of excitation of existing latent design errors within the program is required. In this sense, latent software errors are like latent hardware faults, and techniques that were previously used to measure the probability of failure of hardware due to fault latency can be used to measure the probability of failure of the software. A methodology was developed and applied to a flight control program that was known to operate in a well defined environment. The results indicated that the technique could be used to provide a final validation of the software to a specified reliability level and to evaluate the role of flight test in software validation.
The Skylab food system, waste management system, operational bioinstrumentation, personal hygiene provisions, in-flight medical support system, and the cardiovascular counterpressure garment worn during reentry are described. The medical experiments program provided scientific data and also served as the basis for real-time decisions on flight duration. Premission support, in-flight operational support, and postflight medical activities are surveyed. Measures devised to deal with possible food spoilage, medical instrument damage, and toxic atmosphere caused by the initial failures on the Orbital Workshop (OWS) are discussed. The major medical experiments performed in flight allowed the study of physiological changes as a function of exposure to weightless flight. The experiments included studies of the cardiovascular system, musculoskeletal and fluid/electrolyte balance, sleep, blood, vestibular system, and time and motion studies.
This paper discusses the Space Station assembly sequence planning and development process. It presents the planning methodologies from both historial and current perspectives. It is shown that planning the assembly sequence is a new and unique challenge and its solution requires the simultaneous satisfaction of many diverse variables and constants. The considerations which influence the development of the assembly sequence include launch vehicle integration and lift capabilities, on-orbit assembly flight operations, vehicle flight dynamics, spacecraft system capabilities and resource availability. Many of these considerations are described in this paper. In addition, the examples presented demonstrate the current process for assembly sequence planning and show many of the complex trade-offs that must be performed.
The network operations control area (NOCA) of the space flight operations facility (SFOF) is being upgraded in both hardware and software capabilities to provide the visibility and operability necessary for the operations control function to be performed within the present technological environment of the tracking network. The hardware implementation currently in progress is addressed. It provides a standard operating console with increased display capacity and improved ergonomics as a replacement for the existing, outmoded, circular consoles original to the SFOF.
SNMAT is rule-based expert-system computer program designed to assist personnel in monitoring status of computer network and identifying defective computers, workstations, and other components of network. Also assists in training network operators. Network for SNMAT located at Space Flight Operations Center (SFOC) at NASA's Jet Propulsion Laboratory. Intended to serve as data-reduction system providing windows, menus, and graphs, enabling users to focus on relevant information. SNMAT expected to be adaptable to other computer networks; for example in management of repair, maintenance, and security, or in administration of planning systems, billing systems, or archives.
Pegasus, a three-stage, air-launched, winged space booster was developed to provide fast and efficient commercial launch services for small satellites. The aerodynamic design and analysis of Pegasus was conducted without benefit of wind tunnel tests using only computational aerodynamic and fluid dynamic methods. Flight test data from the first two operational flights of Pegasus are now available, and they provide an opportunity to validate the accuracy of the predicted pre-flight aerodynamic characteristics. Comparisons of measured and predicted flight characteristics are presented and discussed. Results show that the computational methods provide reasonable aerodynamic design information with acceptable margins. Post-flight analyses illustrate certain areas in which improvements are desired.
The Life and Microgravity Spacelab (LMS) payload flew on the Orbiter Columbia on mission STS-78 from June 20th to July 7th, 1996. The LMS payload on STS-78 was dedicated to life sciences and microgravity experiments. Two accelerometer systems managed by the NASA Lewis Research Center (LERC) flew to support these experiments, namely the Orbital Acceleration Research Experiment (OARE) and the Space Acceleration Measurements System (SAMS). In addition, the Microgravity Measurement Assembly (NOAA), managed by the European Space Research and Technology Center (ESA/ESTEC), and sponsored by NASA, collected acceleration data in support of the experiments on-board the LMS mission. OARE downlinked real-time quasi-steady acceleration data, which was provided to the investigators. The SAMS recorded higher frequency data on-board for post-mission analysis. The MMA downlinked real-time quasi-steady as well as higher frequency acceleration data, which was provided to the investigators. The Principal Investigator Microgravity Services (PIMS) project at NASA LERC supports principal investigators of microgravity experiments as they evaluate the effects of varying acceleration levels on their experiments. A summary report was prepared by PIMS to furnish interested experiment investigators with a guide to evaluate the acceleration environment during STS-78, and as a means of identifying areas which require further study. The summary report provides an overview of the STS-78 mission, describes the accelerometer systems flown on this mission, discusses some specific analyses of the accelerometer data in relation to the various activities which occurred during the mission, and presents plots resulting from these analyses as a snapshot of the environment during the mission. Numerous activities occurred during the STS-78 mission that are of interest to the low-gravity community. Specific activities of interest during this mission were crew exercise, radiator deployment, Vernier Reaction Control System (VRCS) reboost, venting operations, Flight Control System (FCS) checkout, rack excitation, operation of the Life Sciences Laboratory Equipment Refrigerator/Freezer (LSLE R/F), operation of the JSC Projects Centrifuge, crew sleep, and attitude changes. The low-gravity environment related to these activities is discussed in the summary report.
As new technologies and procedures are introduced into the National Airspace System, whether they are intended to improve efficiency, capacity, or safety level, the quantification of potential changes in safety levels is of vital concern. Applications of technology can improve safety levels and allow the reduction of separation standards. An excellent example is the Precision Runway Monitor (PRM). By taking advantage of the surveillance and display advances of PRM, airports can run instrument parallel approaches to runways separated by 3400 feet with the same level of safety as parallel approaches to runways separated by 4300 feet using the standard technology. Despite a wealth of information from flight operations and testing programs, there is no readily quantifiable relationship between numerical safety levels and the separation standards that apply to aircraft on final approach. This paper presents a modeling approach to quantify the risk associated with reducing separation on final approach. Reducing aircraft separation, both laterally and longitudinally, has been the goal of several aviation R&D programs over the past several years. Many of these programs have focused on technological solutions to improve navigation accuracy, surveillance accuracy, aircraft situational awareness, controller situational awareness, and other technical and operational factors that are vital to maintaining flight safety. The risk assessment model relates different types of potential aircraft accidents and incidents and their contribution to overall accident risk. The framework links accident risks to a hierarchy of failsafe mechanisms characterized by procedures and interventions. The model will be used to assess the overall level of safety associated with reducing separation standards and the introduction of new technology and procedures, as envisaged under the Free Flight concept. The model framework can be applied to various aircraft scenarios, including parallel and in-trail approaches. This research was performed under contract to NASA and in cooperation with the FAA's Safety Division (ASY).
The Langley Research Center (LaRC) performed atmospheric radiation studies under the SST development program in which important ionizing radiation components were measured and extended by calculations to develop the existing atmospheric ionizing radiation (AIR) model. In that program the measured neutron spectrum was limited to less than 10 MeV by the available 1960-1970 instrumentation. Extension of the neutron spectrum to high energies was made using the LaRC PROPER-3C monte carlo code. It was found that the atmospheric neutrons contributed about half of the dose equivalent and approximately half of the neutron contribution was from high energy neutrons above 10 MeV. Furthermore, monte carlo calculations of solar particle events showed that potential exposures as large as 10-100 mSv/hr may occur on important high latitude routes but acceptable levels of exposure could be obtained if timely descent to subsonic altitudes could be made. The principal concern was for pregnant occupants onboard the aircraft. As a result of these studies the FAA Advisory Committee on the Radiobiological Aspects of the SST recommended: 1. Crew members will have to be informed of their exposure levels 2. Maximum exposures on any flight to be limited to 5 mSv 3. Airborne radiation detection devices for total exposure and exposure rates 4. Satellite monitoring system to provide SST aircraft real-time information on atmospheric radiation levels for exposure mitigation 5. A solar forecasting system to warn flight operations of an impending solar event for flight scheduling and alert status. These recommendations are a reasonable starting point to requirements for the HSCT with some modification reflecting new standards of protection as a result of changing risk coefficients.
The Crew Transportation Operations Standards contains descriptions of ground and flight operations processes and specifications and the criteria which will be used to evaluate the acceptability of Commercial Providers' proposed processes and specifications.
Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACC to which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.
Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACCto which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.
The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.
The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.