Results for “future aviation safety risk” — Page 3

DOE OSTI2023

Ampaire ARPA-e Electric Flight Testbed

A hybrid-electric aircraft flying testbed was developed in this program with the intent to serve as a dedicated, enduring testbed to test and evaluate ARPA-e CIRCUITS Program and other electrified aviation technologies in relevant flight environments. This testbed enabled rapid development cycles of novel and innovative technologies in the electrified aviation space, maturing them from a research lab environment to flying in an aircraft. By providing research groups with the means to test their transformative technologies in a real-world, aircraft environment, the path to validating the safety and reliability of their technologies for future commercial opportunities was greatly accelerated. Three core technologies were integrated and tested: an inverter/motor drive built by the University of Arkansas, a solid-state circuit breaker (iBreaker) built by the Illinois Institute of Technology, and a Flying Capacitor Multi-level (FCML) DC/DC converter built by the University of California, Berkeley. In each of these cases, the requirements established for safety of flight resulted in a holistic approach to the designs, evoking a deeper understanding of the potential failure modes and mitigations necessary to build a robust and flightworthy system. Further, the integration into a hybrid-electric aircraft de-risked the potential electrical and mechanical issues that cannot easily be experienced or replicated in a lab environment. The experiments were also required to undergo representative temperature, shock, and vibration testing as the FAA prescribes for this category of aircraft, facilitating familiarity with the relevant design and test guidelines necessary to commercialize the technologies. This testbed unlocks the massive potential of core power electronics technologies necessary for a safe, robust, and efficient electric aviation future. With quick iterative design, test, and flight cycles, these core technologies are on a quicker path to technology readiness level maturity and commercialization, enabling a more sustainable future for the aviation industry.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2008

The Nation's Needs in Aviation Formal Methods

Aviation, both on-board systems and the National Airspace System, can be transformed by many current and future technical capabilities. To name but a few, improved efficiency may be achieved by integrating functions; robustness may be improved by distributing functions; and safety may be improved by building in risk mitigation through not only redundant, independent systems but also through operational concepts and effective interaction with human operators. This talk will review the key aspects of verification, validation and certification for which formal methods will provide a critical function in enabling truly revolutionary designs to enter the operational community, illustrating successes in formal modeling to date and posing further questions for the formal modeling community.

Pritchett, Amy R.↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRS2023

Concept of Operations for an In-time Aviation Safety Management System (IASMS) for Upper E Airspace

The National Airspace System undergoes continuous change including in the Upper Class E airspace involving increasingly complex operations and a widening diversity of vehicles. To secure a safe future system, the National Academies recommended an In-time Aviation Safety Management System (IASMS) that is extensible to Upper E. Current Air Traffic Management is not cost-effective to scale for future Upper E operations and diversity of vehicles so the Federal Aviation Administration developed an Upper E Traffic Management ConOps to safely integrate the diverse operations and vehicles having different performance characteristics and flight missions without disrupting current operations including space launch and reentry, suborbital flights, supersonic and hypersonic flights, slow moving or stationary unmanned balloons, and long endurance fixed wing vehicles that are slow, stationary, or high speed. IASMS integrates state-of-the-art predictive modeling with reactive and proactive analytics to detect hazards and mitigate risk precursors for Upper E operators. IASMS identifies emergent safety risks exposed by transformation of the NAS with new and increasingly complex operations. Safety intelligence will also expand the data available and offer insight to new approaches for implementing safety improvements to mitigate risk with more seamless “in-time” integration across the policy, risk management, safety assurance, and promotion pillars of SMS.

K Ellis↗

NASA NTRS2023

Research and Technology Challenges for Human Data Analysts in Future Safety Management Systems

Enabling new and novel concepts of operations for Advanced Air Mobility poses an important need to evolve current safety management systems (SMS) and is posited to be realized through advances in Machine Learning (ML) Data Sciences and Artificial Intelligence. The “In-time Aviation Safety Management System” (IASMS) concept of operations supports the need to evolve today’s SMS to become more tailorable, scalable, and interoperable in response to forecasted changes expected for the future airspace system. Key to IASMS is integration of proactive and predictive ML algorithms trained to provide “in time” detection and mitigation of hazards and emergent risks through new methods and novel data types. IASMS research and technology development includes human factors design considerations for these systems to include human-system teaming, innovations in human interfaces and management of complex digital data information, human-system interaction/model-based system engineering, and verification and validation for data assurance and trust.

Chad L Stephens↗

NASA NTRSDate not supplied

Usability of Pre-Flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗

NASA NTRSDate not supplied

Usability of Pre-flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗

NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to “things going right” in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human “success” data generated by the aviation system, from personnel and within the supporting infrastructure.

Holbrook, Jon B.↗

NASA NTRS2017

A Virtual Laboratory for Aviation and Airspace Prognostics Research

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

LVC-DE↗

NASA NTRSDate not supplied

Examining the Changing Roles and Responsibilities of Humans in Envisioned Future In-Time Aviation Safety Management Systems

Advances in technology are enabling new concepts of operations that will trans-form aviation including increasingly autonomous capabilities to handle evolving complex dynamic ecosystems like those associated with Advanced Aerial Mobility. A major challenge is how to ensure today’s safety levels are maintained as the system scales for rapid detection and timely mitigation of safety issues. NASA has developed a concept of operation for In-Time Aviation Safety Management Systems (IASMS) that represents a system-of-system perspective on interconnected capabilities needed to proactively reduce risk in complex operational environments where unknown hazards may exist. As a result, NASA research priorities include under-standing how the balance between humans and automation changes in such envisioned systems, which may lead to novel human-machine interaction paradigms and human-autonomy teaming for informed contingency management.

Lawrence Prinzel↗

NASA NTRS2014

Automated Discovery of Flight Track Anomalies

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high- volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

Matthews, Bryan↗

NASA NTRS2012

Safety Risk Knowledge Elicitation in Support of Aeronautical R and D Portfolio Management: A Case Study

Aviation is a problem domain characterized by a high level of system complexity and uncertainty. Safety risk analysis in such a domain is especially challenging given the multitude of operations and diverse stakeholders. The Federal Aviation Administration (FAA) projects that by 2025 air traffic will increase by more than 50 percent with 1.1 billion passengers a year and more than 85,000 flights every 24 hours contributing to further delays and congestion in the sky (Circelli, 2011). This increased system complexity necessitates the application of structured safety risk analysis methods to understand and eliminate where possible, reduce, and/or mitigate risk factors. The use of expert judgments for probabilistic safety analysis in such a complex domain is necessary especially when evaluating the projected impact of future technologies, capabilities, and procedures for which current operational data may be scarce. Management of an R&D product portfolio in such a dynamic domain needs a systematic process to elicit these expert judgments, process modeling results, perform sensitivity analyses, and efficiently communicate the modeling results to decision makers. In this paper a case study focusing on the application of an R&D portfolio of aeronautical products intended to mitigate aircraft Loss of Control (LOC) accidents is presented. In particular, the knowledge elicitation process with three subject matter experts who contributed to the safety risk model is emphasized. The application and refinement of a verbal-numerical scale for conditional probability elicitation in a Bayesian Belief Network (BBN) is discussed. The preliminary findings from this initial step of a three-part elicitation are important to project management practitioners as they illustrate the vital contribution of systematic knowledge elicitation in complex domains.

Shih, Ann T.↗

NASA NTRS2023

An In-time Aviation Safety Management System (IASMS) Concept of Operations for Vertiport Design and Operations

The National Airspace System is foreseen to undergo revolutionary change with Urban Air Mobility (UAM) and its use of vertiports to transport passengers and cargo. To assure safety with UAM and more broadly with Advanced Air Mobility (AAM), the National Academies recommended an In-time Aviation Safety Management System (IASMS) that is extensible to the design and operation of vertiports. Vertiport designs will scale in several dimensions including physical size and infrastructure depending upon location and in the Services, Functions, and Capabilities required for assuring safety with increasingly complex vertiport designs and operations. These operations will be enabled by evolving technologies including electric vertical takeoff and landing (eVTOL) aircraft for passenger-and cargo-carrying commercial transportation. Within this construct, safety hazards and risk mitigations involving predictive data analytics and modeling will be used. Use cases and future challenges are examined to guide maturation of the IASMS ConOps for vertiports.

K Ellis↗

NASA NTRS2004

Accident Precursor Analysis and Management: Reducing Technological Risk Through Diligence

Almost every year there is at least one technological disaster that highlights the challenge of managing technological risk. On February 1, 2003, the space shuttle Columbia and her crew were lost during reentry into the atmosphere. In the summer of 2003, there was a blackout that left millions of people in the northeast United States without electricity. Forensic analyses, congressional hearings, investigations by scientific boards and panels, and journalistic and academic research have yielded a wealth of information about the events that led up to each disaster, and questions have arisen. Why were the events that led to the accident not recognized as harbingers? Why were risk-reducing steps not taken? This line of questioning is based on the assumption that signals before an accident can and should be recognized. To examine the validity of this assumption, the National Academy of Engineering (NAE) undertook the Accident Precursors Project in February 2003. The project was overseen by a committee of experts from the safety and risk-sciences communities. Rather than examining a single accident or incident, the committee decided to investigate how different organizations anticipate and assess the likelihood of accidents from accident precursors. The project culminated in a workshop held in Washington, D.C., in July 2003. This report includes the papers presented at the workshop, as well as findings and recommendations based on the workshop results and committee discussions. The papers describe precursor strategies in aviation, the chemical industry, health care, nuclear power and security operations. In addition to current practices, they also address some areas for future research.

Phimister, James R.↗

NASA NTRSDate not supplied

Designing for Advanced Aerial Mobility: Human-Autonomy Teaming and In-Time System-Wide Safety Assurance

The continued growth of aviation shall require new innovative technologies and operational concepts to meet the ever-increasing demands on air transportation. The NASA Advanced Air Mobility (AAM) project focuses on emerging aviation markets, such as Urban Air Mobility (UAM). UAM is defined as “...a safe and efficient system for air passenger and cargo transportation within an urban area. It is inclusive of small package delivery and other urban unmanned aerial system services and supports a mix of onboard/ground-piloted and increasingly autonomous operations” ([1]). The AAM project emphasizes technology development and validating system-level concepts and solutions in coordination with other NASA Aeronautics Research Mission Directorate (ARMD) projects to enable UAM metro- and micro-plex vertiport and airspace concepts of operations. The NASA AAM research portfolio includes the concepts of Remote Supervisor-in-Command (RSC) and Fleet and Airspace Manager (FAM) as possible human roles for consumer fleet providers. NASA research in RSC is focused on development of guidelines and standards for remote pilots/operators passively and actively controlling a large fleet of autonomous aircraft. For FAM, flight and ground system concepts and technologies to enable high density homogeneous operations at increased scale from vertiport(s), and coordination with other humans in the systems (e.g., UAM urban airspace manager, Air Traffic Control) are key research areas. The envisioned UAM operations are posited to require autonomous systems to enable functions ranging from fleet and resource management to vehicle control. Although automation has become increasingly sophisticated and ubiquitous in civil aviation, autonomy represents a significant evolution in automation, which has generally been limited in functional scope and capability. As autonomy takes on increasing responsibilities, humans and machines will be required to work together in new and different ways [2], rather than traditional design approaches focused on how machines (i.e., autonomy) can do the work of people. The emerging field of human-autonomy teaming (HAT) represents a comprehensive and prioritized research-driven approach to enable the success of future emerging aviation market applications through capabilities and principles that facilitate humans and machine working and thinking better together. The NASA Transformational Tools and Technologies (TTT) Autonomous System (AS) Sub-project was created to assist with the transition into higher levels of autonomy to enable new modes of air transportation, such as UAM. TTT-AS has identified HAT as a key research need to enable UAM while maintaining today’s ultra-safe aviation system safety levels. The latter challenge has been taken up by the NASA System-Wide Safety (SWS) Project, which recognizes that aviation safety, as it evolves, shall require new ways of thinking about safety to include integration of a wide-range of existing and new safety systems and practices, enhanced tools and technologies, increased access to data and data fusion, improved data analysis capabilities, enhanced in-time risk monitoring and detection, hazard prioritization and mitigation, safety assurance decision-support, and in-time integrated system analytics [3].The operational concept of UAM represents a variety of work that has been termed, “work-as-imagined” to characterize the idea that how people think that work is done and how work is actually done are often not the same [4]. To ensure design success and system safety, looking at “work-as-done” provides a comparative approach toward UAM concept and technology design through examination of corresponding analogs found today in aviation (e.g., on-demand operations) and other transportation domains (e.g., port operations). The paper shall discuss various alternative applications with specific focus on airline operation center (AOC) operations, and unmanned aerial system (UAS) command-and-control to inform scaled-versions of FAM and RSC, respectively, and with consideration of the national airspace system contextual environment. The tenets and principles of the HAT field and current NASA research efforts under the TTT-AS sub-project shall also be described. Finally, the SWS sub-project efforts to develop In-Time System-Wide Safety Assurance (ISSA) and In-Time Safety Management Systems (IASMS) are discussed in terms of how “in-time” safety assurance may be conceptualized for the on-demand mobility air taxi “work-as-imagined” operational concept [5]. As part of this effort, concepts from the emerging field of resilience engineering, are being studied. Traditional approaches to aviation safety have focused on what can go wrong and how to prevent it. Another approach to thinking about system safety should reflect not only “avoiding things that go wrong” (protective safety) but also “ensuring that things go right” (productive safety), that enables a system to exhibit the resilient performance [6] necessary for the success of the future aviation system emerging concepts of operations. The paper shall describe efforts focused on how productive safety and resilience may enable a more complete approach to system safety thinking and design of ISSA and IASMS for UAM. Future directions and research needs shall also be discussed.

resilience↗

NASA NTRS2019

Structural Analysis and Performance-Based Validation of a Composite Wing Spar

Electric-motor powered aircraft possess the ability to operate with efficient energy delivery, but lack the operational range of internal combustion engine powered aircraft. This range limitation requires the use of high aspect ratio, thin-chord wings to minimize aerodynamic drag losses, which results in highly loaded composite spar structures. High aspect ratio wings are required to increase mission durations for a NASA-developed experimental multi-rotor electric powered aircraft denoted as the Scalable Convergent Electric Propulsion Technology and Operations Research (SCEPTOR) or “X-57”. This paper examines the structural performance of the composite main wing spars to validate spar strength using ply-based laminate finite element methods. Geometric scaling of a main spar test-section was initially proposed for proof-testing but sacrificed stability. Ply-based structures modeling with local structural features was implemented as a risk-reduction methodology. Ply-based modeling was selected to augment the conventional “building block” approach to reduce risk, and leverage a “performance-based” approval processes encouraged in Federal Aviation Administration (FAA) design guidance. Therefore, ply-based laminate modeling of the full-scale main spar and forward spar shear-web attachments were subsequently undertaken to determine load path complexity with predicted flight loads. Ply-based modeling included stress concentrations and interlaminate behavior at interface locations that can be obscured in traditional finite element sizing models. Analysis of the wing spar laminate ply-based models compared with bearing test coupon performance was used to reduce future wing assembly proof-testing burden and facilitate performance-based flight hardware safety for the X-57 experimental aircraft.

Moore, James B.↗