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At least 199 records · Page 11

NASA's Aero-Space Technology

This presentation reviews the three pillars and the associated goals of NASA's Aero-Space Technology Enterprise. The three pillars for success are: (1) Global Civil Aviation, (2) Revolutionary Technology Leaps, (3) Advanced Space Transportation. The associated goals of the first pillar are to reduce accidents, emissions, and cost, and to increase the aviation system capacity. The goals of the second pillar are to reduce transoceanic travel time, revolutionize general aviation aircraft, and improve development capacity. The goals associated with the third pillar are to reduce the launch cost for low earth orbit and to reduce travel time for planetary missions. In order to meet these goals NASA must provide next-generation design capability for new and or experimental craft which enable a balance between reducing components of the design cycle by up to 50% and or increasing the confidence in design by 50%. These next-generation design tools, concepts, and processes will revolutionize vehicle development. The presentation finally reviews the importance of modeling and simulation in achieving the goals.

Milstead, Phil↗

Creating Formal Characterizations of Routine Contingency Management in Commercial Aviation

The identification, modelling, and analysis of root causes of accidents and incidents dominate conventional safety management approaches. However, the effect of humans’ safety-producing behavior on the overall resilience of the system is often neglected. Additionally, emerging aviation markets are giving rise to concepts of operation, such as urban air mobility and optionally piloted air cargo operations, that are leading to a shift in locus of control between humans and automation. Without an understanding of the human contribution to safety, it is difficult to assess the effects of these novel role allocations on overall system safety. In this work, safety-producing behaviors are identified and abstracted into resilient performance strategies. Production rules that encapsulate these strategies are then generated and classified in the Soar cognitive architecture. The strategies are then applied to a remotely-operated air cargo example to demonstrate how safe learning is facilitated. The learned rules and strategies are then formally verified.

Safety Critical Systems↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

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.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

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.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

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.

NLP↗

Incident reporting: Its role in aviation safety and the acquisition of human error data

The rationale for aviation incident reporting systems is presented and contrasted to some of the shortcomings of accident investigation procedures. The history of the United State's Aviation Safety Reporting System (ASRS) is outlined and the program's character explained. The planning elements that resulted in the ASRS program's voluntary, confidential, and non-punitive design are discussed. Immunity, from enforcement action and misuse of the volunteered data, is explained and evaluated. Report generation techniques and the ASRS data analysis process are described; in addition, examples of the ASRS program's output and accomplishments are detailed. Finally, the value of incident reporting for the acquisition of safety information, particularly human error data, is explored.

Reynard, W. D.↗

NASA/FAA Tailplane Icing Program: Flight Test Report

This report presents results from research flights that explored the characteristics of an ice-contaminated tailplane using various simulated ice shapes attached to the leading edge of the horizontal tailplane. A clean leading edge provided the baseline case, then three ice shapes were flown in order of increasing severity. Flight tests included both steady state and dynamic maneuvers. The steady state points were 1G wings level and steady heading sideslips. The primary dynamic maneuvers were pushovers to various G-levels; elevator doublets; and thrust transitions. These maneuvers were conducted for a full range of flap positions and aircraft angle of attack where possible. The analysis of this data set has clearly demonstrated the detrimental effects of ice contamination on aircraft stability and controllability. Paths to tailplane stall were revealed through parameter isolation and transition studies. These paths are (1) increasing ice shape severity, (2) increasing flap deflection, (3) high or low speeds, depending on whether the aircraft is in a steady state (high speed) or pushover maneuver (low speed), and (4) increasing thrust. The flight research effort was very comprehensive, but did not examine effects of tailplane design and location, or other aircraft geometry configuration effects. However, this effort provided the role of some of the parameters in promoting tailplane stall. The lessons learned will provide guidance to regulatory agencies, aircraft manufacturers, and operators on ice-contaminated tailplane stall in the effort to increase aviation safety and reduce the fatal accident rate.

Ratvasky, Thomas P.↗

Error Generation in CATS-Based Agents

This research presents a methodology for generating errors from a model of nominally preferred correct operator activities, given a particular operational context, and maintaining an explicit link to the erroneous contextual information to support analyses. It uses the Crew Activity Tracking System (CATS) model as the basis for error generation. This report describes how the process works, and how it may be useful for supporting agent-based system safety analyses. The report presents results obtained by applying the error-generation process and discusses implementation issues. The research is supported by the System-Wide Accident Prevention Element of the NASA Aviation Safety Program.

Callantine, Todd↗

CATS-based Agents That Err

This report describes preliminary research on intelligent agents that make errors. Such agents are crucial to the development of novel agent-based techniques for assessing system safety. The agents extend an agent architecture derived from the Crew Activity Tracking System that has been used as the basis for air traffic controller agents. The report first reviews several error taxonomies. Next, it presents an overview of the air traffic controller agents, then details several mechanisms for causing the agents to err in realistic ways. The report presents a performance assessment of the error-generating agents, and identifies directions for further research. The research was supported by the System-Wide Accident Prevention element of the FAA/NASA Aviation Safety Program.

Callantine, Todd J.↗

Recommendations for a Cockpit Display that Integrates Weather Information with Traffic Information

This effort was supported by the System-Wide Accident Prevention element of NASA s Aviation Safety Program. This document may serve as a first step toward the goal of integrating traffic, weather, and terrain information; it provides recommendations for a cockpit display that integrates weather information with traffic information. While some of the recommendations are general enough to be used for any type of operations, these recommendations are targeted for Federal Aviation Regulations Part 121 Operations. The document is organized in the following manner. First, weather information is discussed as an independent subject matter, and recommendations are presented for presenting weather in the cockpit. Second, traffic is discussed independently, but this discussion essentially reviews work on the display of traffic in the cockpit. Third, recommendations for the cockpit integration of weather and traffic information are discussed. Fourth, several research groups are recognized for their efforts in developing systems that are relevant to the current discussion. Finally, closing remarks provide suggestions for future efforts.

Comerford, Doreen A.↗

Systems Analysis of NASA Aviation Safety Program: Final Report

A three-month study (February to April 2010) of the NASA Aviation Safety (AvSafe) program was conducted. This study comprised three components: (1) a statistical analysis of currently available civilian subsonic aircraft data from the National Transportation Safety Board (NTSB), the Federal Aviation Administration (FAA), and the Aviation Safety Information Analysis and Sharing (ASIAS) system to identify any significant or overlooked aviation safety issues; (2) a high-level qualitative identification of future safety risks, with an assessment of the potential impact of the NASA AvSafe research on the National Airspace System (NAS) based on these risks; and (3) a detailed, top-down analysis of the NASA AvSafe program using an established and peer-reviewed systems analysis methodology. The statistical analysis identified the top aviation "tall poles" based on NTSB accident and FAA incident data from 1997 to 2006. A separate examination of medical helicopter accidents in the United States was also conducted. Multiple external sources were used to develop a compilation of ten "tall poles" in future safety issues/risks. The top-down analysis of the AvSafe was conducted by using a modification of the Gibson methodology. Of the 17 challenging safety issues that were identified, 11 were directly addressed by the AvSafe program research portfolio.

Jones, Sharon M.↗

Aviation Trends Related to Atmospheric Environment Safety Technologies Project Technical Challenges

Current and future aviation safety trends related to the National Aeronautics and Space Administration's Atmospheric Environment Safety Technologies Project's three technical challenges (engine icing characterization and simulation capability; airframe icing simulation and engineering tool capability; and atmospheric hazard sensing and mitigation technology capability) were assessed by examining the National Transportation Safety Board (NTSB) accident database (1989 to 2008), incidents from the Federal Aviation Administration (FAA) accident/incident database (1989 to 2006), and literature from various industry and government sources. The accident and incident data were examined for events involving fixed-wing airplanes operating under Federal Aviation Regulation (FAR) Parts 121, 135, and 91 for atmospheric conditions related to airframe icing, ice-crystal engine icing, turbulence, clear air turbulence, wake vortex, lightning, and low visibility (fog, low ceiling, clouds, precipitation, and low lighting). Five future aviation safety risk areas associated with the three AEST technical challenges were identified after an exhaustive survey of a variety of sources and include: approach and landing accident reduction, icing/ice detection, loss of control in flight, super density operations, and runway safety.

Reveley, Mary S.↗

An Analysis of U.S. Civil Rotorcraft Accidents by Cost and Injury (1990-1996)

A study of rotorcraft accidents was conducted to identify safety issues and research areas that might lead to a reduction in rotorcraft accidents and fatalities. The primary source of data was summaries of National Transportation Safety Board (NTSB) accident reports. From 1990 to 1996, the NTSB documented 1396 civil rotorcraft accidents in the United States in which 491 people were killed. The rotorcraft data were compared to airline and general aviation data to determine the relative safety of rotorcraft compared to other segments of the aviation industry. In depth analysis of the rotorcraft data addressed demographics, mission, and operational factors. Rotorcraft were found to have an accident rate about ten times that of commercial airliners and about the same as that of general aviation. The likelihood that an accident would be fatal was about equal for all three classes of operation. The most dramatic division in rotorcraft accidents is between flights flown by private pilots versus professional pilots. Private pilots, flying low cost aircraft in benign environments, have accidents that are due, in large part, to their own errors. Professional pilots, in contrast, are more likely to have accidents that are a result of exacting missions or use of specialized equipment. For both groups judgement error is more likely to lead to a fatal accident than are other types of causes. Several approaches to improving the rotorcraft accident rate are recommended. These mostly address improvement in the training of new pilots and improving the safety awareness of private pilots.

Iseler, Laura↗

U.S. Civil Rotorcraft Accidents, 1963 through 1997

The U.S. National Transportation Safety Board (NTSB) has recorded 8,436 rotorcraft accidents during the period mid - 1963 through the end of 1997. Review and analysis of the NTSB summary narrative for each accident has been completed. In addition, FAA (Federal Aviation Administration) counts of the growing registered rotorcraft fleet over this period has obtained. Taken together, a large and informative data base is now available, which indicates that the accident rate (on a per airframe basis) has changed very little since the mid 1970s. The data base, even in the summary form provided by this paper, offers suggestions for safer designs and improved flight operations. For analysis purposes, each accident has been placed in one of 21 top level categories as defined by the NTSB. Analysis of this grouping shows that 70 percent of rotorcraft accidents are associated with four categories. The accident count in these top four categories are: (1) 2,408 Loss of engine power (2) 1,322 In flight collision with object (3) 1,114 Loss of control (4) 1,083 Airframe/component/system failure or malfunction. Single engine rotorcraft dominate these accident statistics because of their sheer numbers over the study period. One-third of the loss of engine power accidents with these aircraft is fuel/air mixture related and fuel exhaustion is a common event. This appears to be the case whether a piston or turbine engine is installed. This paper provides similar study results in the other major mishap categories. It shows that both minor and major design and flight operations changes can -- and should -- be made to reduce rotorcraft accidents in the future. The paper outlines these changes and suggests how they may be made.

Harris, Franklin D.↗

Aviation Weather Information Overview and Status

This paper presents the weather accident prevention project review during the period of June 5, through June 7, 2001. The topics include: 1) Background; 2) Guidance; 3) Plan; 4) System Elements; 5) AWIN System; 6) Market Segments; 7) Technology Development Level; 8) Aviation Safety Program Organization; 9) Partnerships; 10) NASA Facilities; 11) Timeline; 12) AWIN Research Areas; and 13) Cooperative Research with FAA. This paper is in viewgraph form.

Stough, Paul↗

NASA Aviation Safety Program Systems Analysis/Program Assessment Metrics Review

The goal of this project is to evaluate the metrics and processes used by NASA's Aviation Safety Program in assessing technologies that contribute to NASA's aviation safety goals. There were three objectives for reaching this goal. First, NASA's main objectives for aviation safety were documented and their consistency was checked against the main objectives of the Aviation Safety Program. Next, the metrics used for technology investment by the Program Assessment function of AvSP were evaluated. Finally, other metrics that could be used by the Program Assessment Team (PAT) were identified and evaluated. This investigation revealed that the objectives are in fact consistent across organizational levels at NASA and with the FAA. Some of the major issues discussed in this study which should be further investigated, are the removal of the Cost and Return-on-Investment metrics, the lack of the metrics to measure the balance of investment and technology, the interdependencies between some of the metric risk driver categories, and the conflict between 'fatal accident rate' and 'accident rate' in the language of the Aviation Safety goal as stated in different sources.

Louis, Garrick E.↗

Single pilot IFR accident data analysis

The aircraft accident data recorded and maintained by the National Transportation Safety Board for 1964 to 1979 were analyzed to determine what problems exist in the general aviation single pilot instrument flight rules environment. A previous study conducted in 1978 for the years 1964 to 1975 provided a basis for comparison. The purpose was to determine what changes, if any, have occurred in trends and cause-effect relationships reported in the earlier study. The increasing numbers have been tied to measures of activity to produce accident rates which in turn were analyzed in terms of change. Where anomalies or unusually high accident rates were encountered, further analysis was conducted to isolate pertinent patterns of cause factors and/or experience levels of involved pilots. The bulk of the effort addresses accidents in the landing phase of operations. A detailed analysis was performed on controlled/uncontrolled collisions and their unique attributes delineated. Estimates of day vs. night general aviation activity and accident rates were obtained.

Harris, D. F.↗

IMPACT RESEARCH

Faa impact research - aviation medicine, human tolerance, acceleration stress, aircraft accidents

AEROSPACE MEDICINE↗