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NASA NTRS2016

Isolated Open Rotor Noise Prediction Assessment Using the F31A31 Historical Blade Set

In an effort to mitigate next-generation fuel efficiency and environmental impact concerns for aviation, open rotor propulsion systems have received renewed interest. However, maintaining the high propulsive efficiency while simultaneously meeting noise goals has been one of the challenges in making open rotor propulsion a viable option. Improvements in prediction tools and design methodologies have opened the design space for next generation open rotor designs that satisfy these challenging objectives. As such, validation of aerodynamic and acoustic prediction tools has been an important aspect of open rotor research efforts. This paper describes validation efforts of a combined computational fluid dynamics and Ffowcs Williams and Hawkings equation methodology for open rotor aeroacoustic modeling. Performance and acoustic predictions were made for a benchmark open rotor blade set and compared with measurements over a range of rotor speeds and observer angles. Overall, the results indicate that the computational approach is acceptable for assessing low-noise open rotor designs. Additionally, this approach may be used to provide realistic incident source fields for acoustic shielding/scattering studies on various aircraft configurations.

Nark, Douglas M.↗

NASA NTRS2016

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats.

Complex Networks↗

NASA NTRS1997

Evaluation of Flight Attendant Technical Knowledge

Accident and incident reports have indicated that flight attendants have numerous opportunities to provide the flight-deck crew with operational information that may prevent or lessen the severity of a potential problem. Additionally, as carrier fleets transition from three person to two person flight-deck crews, the reliance upon the cabin crew for the transfer of this information may increase further. Recent research indicates that flight attendants do not feel confident in their ability to describe mechanical parts or malfunctions of the aircraft, and the lack of flight attendant technical training has been referenced in a number of recent reports. Chute and Wiener describe five factors which may produce communication barriers between cockpit and cabin crews: the historical background of aviation, the physical separation of the two crews, psychosocial issues, regulatory factors, and organizational factors. By examining these areas of division we can identify possible bridges and address the implications of deficient cockpit/cabin communication on flight safety. Flight attendant operational knowledge may provide some mitigation of these barriers. The present study explored both flight attendant technical knowledge and flight attendant and pilot expectations of flight attendant technical knowledge. To assess the technical knowledge of cabin crewmembers, 177 current flight attendants from two U.S. carriers voluntarily completed a 13-item technical quiz. To investigate expectations of flight attendant technical knowledge, 181 pilots and a second sample of 96 flight attendants, from the same two airlines, completed surveys designed to capture each group's expectations of operational knowledge required of flight attendants. Analyses revealed several discrepancies between the present level of flight attendant operational knowledge and pilots' and flight attendants' expected and desired levels of technical knowledge. Implications for training will be discussed.

Dunbar, Melisa G.↗

NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗

NASA NTRS1997

Studies of the Interactions Between Vestibular Function and Tactual Orientation Display Systems

When humans experience conditions in which internal vestibular cues to movement or spatial location are challenged or contradicted by external visual information, the result can be spatial disorientation, often leading to motion sickness. Spatial disorientation can occur in any situation in which the individual is passively moved in the environment, but is most common in automotive, aircraft, or undersea travel. Significantly, the incidence of motion sickness in space travel is great: The majority of individuals in Shuttle operations suffer from the syndrome. Even after the space-sickness-producing influences of spatial disorientation dissipate, usually within several days, there are other situations in which, because of the absence of reliable or familiar vestibular cues, individuals in space still experience disorientation, resulting in a reliance on the already preoccupied sense of vision. One possible technique to minimize the deleterious effects of spatial disorientation might be to present attitude information (including orientation, direction, and motion) through another less-used sensory modality - the sense of touch. Data from experiences with deaf and blind persons indicate that this channel can provide useful communication and mobility information on a real-time basis. More recently, technologies have developed to present effective attitude information to pilots in situations in which dangerously ambiguous and conflicting visual and vestibular sensations occur. This summers project at NASA-Johnson Space Center will evaluate the influence of motion-based spatial disorientation on the perception of tactual stimuli representing veridical position and orientation information, presented by new dynamic vibrotactile array display technologies. In addition, the possibility will be explored that tactile presentations of motion and direction from this alternative modality might be useful in mitigating or alleviating spatial disorientation produced by multi-axis rotatory systems, monitored by physiological recording techniques developed at JSC.

Cholewiak, Roger W.↗

NASA NTRSDate not supplied

Long-Term Health Risk Quantification

Astronauts face hazards during spaceflight, including space radiation exposure, isolation and confinement, traveling far distances from Earth, reduced gravity levels, and closed and hostile environments. These hazards drive the definition of human health and performance risks associated with spaceflight. NASA’s Human System Risk Board maintains the human spaceflight risk posture for in-mission risks, as well as post-flight, Long-Term Health (LTH)risks potentially occurring later in the astronaut’s life. LTH risk encompasses the timeframe from immediately post-flight, through the rest of an astronaut’s career, through retirement, and until death. Possible LTH risk outcomes include the time and interventions needed for the astronaut to return to preflight physiological states after experiencing spaceflight hazards and recovery from any in-mission medical events that persist into the post-flight timeframe. It includes chronic complications that may arise due to experiencing in-flight medical conditions or injuries and medical conditions that occur later in life with a higher probability of occurrence or with more severity because of their spaceflight exposure. Finally, LTH risk outcomes can also include a reduction in life expectancy due to spaceflight exposures. There have been 144 medical conditions identified by NASA’s Lifetime Surveillance of Astronaut Health team to be associated with LTH risk. Epidemiological studies have been performed for some of these conditions to determine if astronauts suffer from an increased prevalence or severity of the condition due to their spaceflight experience compared to a comparable cohort .Differences in astronaut mortality or morbidity due to spaceflight experience were not detected in several of these studies. There were two cases where a modest increase in the incidence rate of astronaut LTH outcomes was detected. The first suggested an increase in the incidence of melanoma cases in astronauts, where the number of cases in astronauts were similar to the elevated number of cases observed in airplane pilots. The second provided some evidence of elevated numbers of cardiovascular disease events in astronauts compared to an appropriate healthy comparator cohort, which may warrant additional investigation. The lack of detection of LTH risk outcomes should not ease concerns about astronaut LTH. The studies highlighted here constitute only a small portion of the potential LTH conditions that could occur. Once epidemiological studies are performed on all conditions, significant findings may be detected. The analysis of astronaut LTH also suffers from limited numbers of data points because of the limited numbers of astronauts overall and the even fewer who have reached an age where LTH outcomes may begin to manifest. As shuttle and ISS astronauts begin to age and increase the feasibility of analysis, LTH outcomes may be detected. An application of risk quantification is the use of risk metrics within trade studies for resource prioritization and decision making. Trade studies regarding countermeasures to LTH risk outcomes would benefit from a quantification of LTH risk. NASA has ground-based processes in place such as astronaut screening and access to continuous medical monitoring and care during and after their astronaut career which are the main methods for mitigating LTH risk. In-mission countermeasures, such as acceptable levels of medical care and available countermeasures to counter spaceflight related physiological decrements, can mitigate a poor health and performance status immediately post-flight. Identifying appropriate risk metrics, obtaining valid quantities for them, and tying them to LTH countermeasures are necessary steps for realizing their use in trade studies. This presentation will highlight the challenges associated with the identification, quantification, and utilization of LTH risk metrics

Beth Lewandowski↗

NASA NTRSDate not supplied

Suited User Incident Tracking System

The Suited User Incident Tracking System (SUITS) tracks all suited exposures during terrestrial analog simulations and trainings with the objective of recording any related issues, pains, or discomforts that arise from the exposure. This information is vital for informing where, how, and why issues are occurring during suited extravehicular activity (EVA) training and operations. The outputs of SUITS will be the foundation for the newly formed Suited Anomaly Assessment Team (SAAT) which consists of a broad range of EVA stakeholders including suit engineers, biomechanists, ergonomists, flight surgeons, athletic trainers, and physiologists. This team has been formed to provide robust, wholistic solutions to suit anomalies and issues identified by SUITS. SUITS data collected from September 2017 to June 2022 was analyzed for issue frequency and severity for each body location and plotted to visualize where issues were occurring. Clustering techniques using a k-modes approach were also applied to the data set to generate profiles of populations that experienced issues in the suit and populations that did not. A key finding identified by the SUITs analysis was that suited issues translate from predominantly upper body areas in microgravity space suits and associated analog simulations to lower body areas in planetary space suits and analog simulations. In addition, specific injury frequency and severity metrics were identified that will help drive requirements for commercially provided space suits. The SAAT will continue to meet on a biannual basis to identify trends in issues and injuries from the SUITS database to evaluate and develop solution strategies where needed for the purposes of suit design, EVA operations optimization, injury characterization and mitigation, and personalized improvements for human-suit interactions.

K. Dolick↗

NASA NTRS2014

Nuclear Thermal Rocket (NTR) Development Risk Communication

There are clear advantages of development of a Nuclear Thermal Rocket (NTR) for a crewed mission to Mars. NTR for in-space propulsion enables more ambitious space missions by providing high thrust at high specific impulse (approximately 900 sec) that is 2 times the best theoretical performance possible for chemical rockets. Missions can be optimized for maximum payload capability to take more payload with reduced total mass to orbit; saving cost on reduction of the number of launch vehicles needed. Or missions can be optimized to minimize trip time significantly to reduce the deep space radiation exposure to the crew. NTR propulsion technology is a game changer for space exploration. However, "NUCLEAR" is a word that is feared and vilified by some groups and the hostility towards development of any nuclear systems can meet great opposition by the public as well as from national leaders and people in authority. Communication of nuclear safety will be critical to the success of the development of the NTR. Why is there a fear of nuclear? A bomb that can level a city is a scary weapon. The first and only times the Nuclear Bomb was used in a war was on Hiroshima and Nagasaki during World War 2. The "Little Boy" atomic bomb was dropped on Hiroshima on August 6, 1945 and the "Fat Man" on Nagasaki 3 days later on August 9th. Within the first 4 months of bombings, 90- 166 thousand people died in Hiroshima and 60-80 thousand died in Nagasaki. It is important to note for comparison that over 500 thousand people died and 5 million made homeless due to strategic bombing (approximately 150 thousand tons) of Japanese cities and war assets with conventional non-nuclear weapons between 1942- 1945. A major bombing campaign of "firebombing" of Tokyo called "Operation Meetinghouse" on March 9 and 10 consisting of 334 B-29's dropped approximately1,700 tons of bombs around 16 square mile area and over 100 thousand people have been estimated to have died. The declaration of death is very clear for conventional weapons and then the declaration of death due to radiation becomes vague and unclear. This may have been due to people mis-understanding the dangers and effects of radiation when assessing the damage and harm to people initially, but it is also become insidious when expressing opposition to nuclear energy. A nuclear radiation accident can be scary due to the power involved and the fear of radiation release. The International Atomic Energy Agency defines a nuclear and radiation accident a "an event that has led to significant consequences to people, the environment or the facility." There have been 3 commercial nuclear reactor accidents (Chernobyl, Three Mile Island, and Fukushima) that stand out to the public and much of the information about the result and impact to workers, environment, and public can be misleading. Often information is presented without clear correlation with radiation and other pertinent information is left out presenting a very scary situation to affect the emotions of the reader. A very boring but "critically acclaimed" movie was made in 1979 called "The China Syndrome" starring Jane Fonda and Jack Lemmon. The film was released on March 16, 1979, 12 days before the 3-Mile Island nuclear accident in Pennsylvania. The basis from the movie was from a few nuclear plant incidents and in particular, the Brown's Ferry Alabama Power Plant fire. In one scene from the movie, a physicist Dr. Elliott Lowell played by Donald Hotton states that a China Syndrome event would make "an area the size of Pennsylvania" permanently uninhabitable. Real serious nuclear incidents like Chernobyl and Fukushima are often sited to make people fear the consequences of using nuclear power. However, the consequences are at best poorly communicated and at worst fictitiously inflated to instigate social unrest against nuclear power. There is an article being circulated on Facebook with a title "28 Signs that the (US) West Coast is being absolutely fried with nuclear radiation from Fukushima" which focus on mis-information and fear mongering. Nuclear power and NTR are powerful resources that can open many doors for future prosperity and capability. With great power comes great responsibility. Radiation and its effects need to be better understood, quantified, and communicated. A human mission to mars has its own risks of deep space radiation and is considered a considerable risk at 400 milli-Sieverts per year in deep space and 245 milli-Sieverts per year on the surface of Mars as measured by the Mars Curiosity mission. Although these quantities of ionizing radiation are within the astronaut career limit, it exceeds the yearly average amounts of ionizing radiation. Astronaut crews have experienced these levels of radiation before, but for durations shorter than a year, and a mission to Mars could possibly be 3 years in length. There is also evidence that people can comfortably handle higher levels of ionizing radiation where the radiation occurs naturally like Ramsar, Iran when people can experience 270 milli-Sieverts per year. A risk posture that the development, test, and flight of an NTR will meet opposition from groups who oppose nuclear energy must be likely and the impact can be sever to the effort. Active risk mitigation must be taken for an NTR full-scale development project. The NTR design must take into account safety for transport and off nominal conditions. Nuclear fuel element must consider containment of fission products and Low Enriched Uranium (LEU) that may meet less opposition should be considered for safety and security reasons. Even though testing was conducted on Rover/NERVA safely and successfully in the 60's with exhaust sent heavenward in to open air, modern testing of NTR must consider full containment and no release of ionizing radiation to the public and must meet the current requirement of no more than 0.1 milli-Sieverts per year to the public. 0.1 milli-Sieverts is equivalent to eating one banana or a 20 hour plane flight. Good communication with the public and regulatory agencies will be essential to show that all effort is applied toward protection to the public and astronauts. The inspiring endeavor to put humans on Mars to study the planet, search for life, and learn more about this Solar System will be full of risks but it will be worth it. NTR will be worth the development effort if it allows humans to explore our Solar System.

Kim, Tony↗

NASA NTRS2024

Testing of Advanced Capabilities to Enable In-time Safety Management and Assurance for Future Flight Operations

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

sUAS↗

NASA NTRS2016

Management of Asymptomatic Renal Stones in Astronauts

Introduction: Management guidelines were created to screen and manage asymptomatic renal stones in U.S. astronauts. The risks for renal stone formation in astronauts due to bone loss and hypercalcuria are unknown. Astronauts have a stone risk which is about the same as commercial aviation pilots, which is about half that of the general population. However, proper management of this condition is still crucial to mitigate health and mission risks in the spaceflight environment. Methods: An extensive review of the literature and current aeromedical standards for the monitoring and management of renal stones was done. The NASA Flight Medicine Clinic's electronic medical record and Longitudinal Survey of Astronaut Health were also reviewed. Using this work, a screening and management algorithm was created that takes into consideration the unique operational environment of spaceflight. Results: Renal stone screening and management guidelines for astronauts were created based on accepted standards of care, with consideration to the environment of spaceflight. In the proposed algorithm, all astronauts will receive a yearly screening ultrasound for renal calcifications, or mineralized renal material (MRM). Any areas of MRM, 3 millimeters or larger, are considered a positive finding. Three millimeters approaches the detection limit of standard ultrasound, and several studies have shown that any stone that is 3 millimeters or less has an approximately 95 percent chance of spontaneous passage. For mission-assigned astronauts, any positive ultrasound study is followed by low-dose renal computed tomography (CT) scan, and flexible ureteroscopy if CT is positive. Other specific guidelines were also created. Discussion: The term "MRM" is used to account for small areas of calcification that may be outside the renal collecting system, and allows objectivity without otherwise constraining the diagnostic and treatment process for potentially very small calcifications of uncertain significance. However, a small asymptomatic MRM or stone within the renal collecting system may become symptomatic, and so affect launch and flight schedules, cause incapacitation during flight, and ultimately require medical evacuation. For exploration class missions, evacuation is unlikely. The new screening and management algorithm allows better management of mission risks, and will define the true incidence of renal stones in U.S. astronauts. This information will be used to refine future screening, countermeasures and treatment methods; and will also inform the needed capabilities to be flown on exploration-class missions.

Reyes, David↗

NASA NTRS2016

Management of Asymptomatic Renal Stones in Astronauts

Reyes, David↗

NASA NTRS2000

Analysis of Complexity Evolution Management and Human Performance Issues in Commercial Aircraft Automation Systems

Autoflight systems in the current generation of aircraft have been implicated in several recent incidents and accidents. A contributory aspect to these incidents may be the manner in which aircraft transition between differing behaviours or 'modes.' The current state of aircraft automation was investigated and the incremental development of the autoflight system was tracked through a set of aircraft to gain insight into how these systems developed. This process appears to have resulted in a system without a consistent global representation. In order to evaluate and examine autoflight systems, a 'Hybrid Automation Representation' (HAR) was developed. This representation was used to examine several specific problems known to exist in aircraft systems. Cyclomatic complexity is an analysis tool from computer science which counts the number of linearly independent paths through a program graph. This approach was extended to examine autoflight mode transitions modelled with the HAR. A survey was conducted of pilots to identify those autoflight mode transitions which airline pilots find difficult. The transitions identified in this survey were analyzed using cyclomatic complexity to gain insight into the apparent complexity of the autoflight system from the perspective of the pilot. Mode transitions which had been identified as complex by pilots were found to have a high cyclomatic complexity. Further examination was made into a set of specific problems identified in aircraft: the lack of a consistent representation of automation, concern regarding appropriate feedback from the automation, and the implications of physical limitations on the autoflight systems. Mode transitions involved in changing to and leveling at a new altitude were identified across multiple aircraft by numerous pilots. Where possible, evaluation and verification of the behaviour of these autoflight mode transitions was investigated via aircraft-specific high fidelity simulators. Three solution approaches to concerns regarding autoflight systems, and mode transitions in particular, are presented in this thesis. The first is to use training to modify pilot behaviours, or procedures to work around known problems. The second approach is to mitigate problems by enhancing feedback. The third approach is to modify the process by which automation is designed. The Operator Directed Process forces the consideration and creation of an automation model early in the design process for use as the basis of the software specification and training.

Vakil, Sanjay S.↗

NASA NTRS2015

High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort

Human hands play a significant role during Extravehicular Activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. Because of this high frequency usage, hand and arm related injuries are known to occur during EVA and EVA training in the NBL. The primary objectives of this investigation were to: 1) document all known EVA glove related injuries and circumstances of these incidents, 2) determine likely risk factors, and 3) recommend interventions where possible that could be implemented in the current and future glove designs. METHODS: The investigation focused on the discomforts and injuries of U.S. crewmembers who had worn the pressurized Extravehicular Mobility Unit (EMU) spacesuit and experienced 4000 Series or Phase VI glove related incidents during 1981 to 2010 for either EVA ground training or in-orbit flight. We conducted an observational retrospective case-control investigation using 1) a literature review of known injuries, 2) data mining of crew injury, glove sizing, and hand anthropometry databases, 3) descriptive statistical analyses, and finally 4) statistical risk correlation and predictor analyses to better understand injury prevalence and potential causation. Specific predictor statistical analyses included use of principal component analyses (PCA), multiple logistic regression, and survival analyses (Cox proportional hazards regression). Results of these analyses were computed risk variables in the forms of odds ratios (likelihood of an injury occurring given the magnitude of a risk variable) and hazard ratios (likelihood of time to injury occurrence). Due to the exploratory nature of this investigation, we selected predictor variables significant at p≤0.15. RESULTS: Through 2010, there have been a total of 330 NASA crewmembers, from which 96 crewmembers performed 322 EVAs during 1981-2010, resulting in 50 crewmembers being injured inflight and 44 injured during 11,704 ground EVA training events. Of the 196 glove related injury incidents, 106 related to EVA and 90 to EVA training. Over these 196 incidents, 277 total injuries (126 flight; 151 training) were reported and were then grouped into 23 types of injuries. Of EVA flight injuries, 65% were commonly reported to the hand (in general), metacarpophalangeal (MCP) joint, and finger (not including thumb) with fatigue, abrasion, and paresthesia being the most common injury types (44% of total flight injuries). Training injuries totaled to more than 70% being distributed to the fingernail, MCP joint, and finger crotch with 88% of the specific injuries listed as pain, erythema, and onycholysis. Of these training injuries, when reporting pain or erythema, the most common location was the index finger, but when reporting onycholysis, it was the middle finger. Predictor variables specific to increased risk of onycholysis included: female sex (OR=2.622), older age (OR=1.065), increased duration in hours of the flight or training event (OR=1.570), middle finger length differences in inches between the finger and the EVA glove (OR=7.709), and use of the Phase VI glove (OR=8.535). Differentiation between training and flight and injury reporting during 2002-2004 were significant control variables. For likelihood of time to first onycholysis injury, there was a 24% reduction in rate of reporting for each year increase in age. Also, more experienced crewmembers, based on number of EVA flight or training events completed, were less likely to report an onycholysis injury (3% less for every event). Longer duration events also found reporting rates to occur 2.37 times faster for every hour of length. Crewmembers with larger hand size reported onycholysis 23% faster than those with smaller hand size. Finally, for every 1/10th of an inch increase in difference between the middle finger length and the glove, the rate of reporting increased by 60%. DISCUSSION: One key finding was that the Series 4000 glove had a lower injury risk than the Phase VI, which provides a platform for further evaluation. General interventions that reduce hand overexertion and repetitive use exposure through tool development, procedural changes and shorter exposures may be one mitigation path, but due to the way the training event times were reported, we cannot provide a guideline for a specific event duration change. When the finger length was different from the glove length, the risk of injury increased indicating that the use of larger finger take-ups could be contributing to injury and therefore may not be recommended. Prior to this investigation, there was one previous investigation indicating hand anthropometry may be related to onycholysis. We found different hand anthropometry variables indicated by this investigation as compared to the prior, specifically differences in middle finger length compared to glove finger length, which point more towards a sizing issue than a specific anthropometry issue. Additionally, although this investigation has identified sizing as an issue, the force and environmental-related variables of the EVA glove that could also cause injury were not accounted for.

Reid, C. R.↗

NASA NTRSDate not supplied

Development, Validation and Approval of A Planetary Extravehicular Activity Prebreathe Protocol: NASA Exploration Atmosphere Tests 1 & 2

INTRODUCTION: Denitrogenation prebreathe protocols used to mitigate DCS risk for Space Shuttle and International Space Station EVAs are validated for the microgravity environment, but the significantly increased risk of DCS during equivalent ambulatory surface EVAs make these protocols inapplicable to planetary/Lunar missions as planned by the Artemis program. Living in an “Exploration Atmosphere” of 56.5 kPa (8.2 psia), 34% O2, 66% N2 has been recommended by NASA for future Moon and Mars missions as a compromise that balances pre-EVA prebreathe duration, hypoxia, and flammability risk, assuming a 29.6 kPa (4.3 psi) spacesuit. A prebreathe validation campaign at NASA’s Johnson Space Center in 2022–2023 has aimed to validate the prebreathe durations and is being operationalized by NASA for use in upcoming Lunar EVAs. METHODS: Twelve volunteers lived in a hyboparic chamber for 11 days with an “exploration atmosphere” of 56.6kPa/34% O2 66% N2. Subjects acclimated to this atmosphere for 48 hrs and thereafter participated in five 6-hour simulated EVAs at 34kPa/85% O2 / 15% N2 over the course of 11 days. Prior to each simulated EVA, subjects underwent a 20-minute prebreathe at 85% O2. The EVA simulation was designed to include tasks that are physically and ergonomically representative of future planetary EVAs, proportionate to the subject’s VO2max. Decompression stress was evaluated during the simulated EVA by serial doppler and echocardiographs alternating every 15 min, as well as clinical monitoring for DCS signs/symptoms. Venous gas emboli (VGE) and DCS outcomes were verified against NASA Standard 3001, which guides allowable prebreathe protocol acceptance criteria. RESULTS AND DISCUSSION: Venous gas emboli (VGE) were identified during EVAs. No Grade IV VGE were observed. Two cases of mild, Type I DCS were identified in the subjects over the course of 50 EVA exposures. Ten planned EVA exposures were eliminated due to mask fit, metabolic rate, or subject safety concerns. One subject was removed from the study due to presence of left ventricular VGE. Additionally, two doppler techs also experienced DCS, and one case of hypoxia was noted. All cases of DCS resolved with treatment. No cases of severe DCS were observed. The observed incidence (4%, 1.1–13.5% at 95% confidence) met the NASA Standard 3001 criteria leading to the transition of this protocol from research to operational use for upcoming Lunar missions.

Exploration Atmosphere↗

NASA NTRS2016

Tailoring a Human Reliability Analysis to Your Industry Needs

Companies at risk of accidents caused by human error that result in catastrophic consequences include: airline industry mishaps, medical malpractice, medication mistakes, aerospace failures, major oil spills, transportation mishaps, power production failures and manufacturing facility incidents. Human Reliability Assessment (HRA) is used to analyze the inherent risk of human behavior or actions introducing errors into the operation of a system or process. These assessments can be used to identify where errors are most likely to arise and the potential risks involved if they do occur. Using the basic concepts of HRA, an evolving group of methodologies are used to meet various industry needs. Determining which methodology or combination of techniques will provide a quality human reliability assessment is a key element to developing effective strategies for understanding and dealing with risks caused by human errors. There are a number of concerns and difficulties in "tailoring" a Human Reliability Assessment (HRA) for different industries. Although a variety of HRA methodologies are available to analyze human error events, determining the most appropriate tools to provide the most useful results can depend on industry specific cultures and requirements. Methodology selection may be based on a variety of factors that include: 1) how people act and react in different industries, 2) expectations based on industry standards, 3) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 4) type and availability of data, 5) how the industry views risk & reliability, and 6) types of emergencies, contingencies and routine tasks. Other considerations for methodology selection should be based on what information is needed from the assessment. If the principal concern is determination of the primary risk factors contributing to the potential human error, a more detailed analysis method may be employed versus a requirement to provide a numerical value as part of a probabilistic risk assessment. Industries involved with humans operating large equipment or transport systems (ex. railroads or airlines) would have more need to address the man machine interface than medical workers administering medications. Human error occurs in every industry; in most cases the consequences are relatively benign and occasionally beneficial. In cases where the results can have disastrous consequences, the use of Human Reliability techniques to identify and classify the risk of human errors allows a company more opportunities to mitigate or eliminate these types of risks and prevent costly tragedies.

DeMott, D. L.↗

NASA NTRSDate not supplied

A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗