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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

Observing System Simulations for the AOS Mission

The Earth System Observatory (ESO) is NASA’s response to the recommendations of the 2017 Earth Sciences Decadal Survey conducted by the US National Academy of Sciences, Engineering and Medicine. The ESO is being conceived as a set of fully integrated missions addressing 4 main Earth science focus areas including aerosols, clouds, convection and precipitation (jointly re-ferred to as AOS, the Atmosphere Observing System). ESO ground breaking observations will provide critical measurements to address societally relevant problems in climate change, natural hazard mitiga-tion, fighting forest fires, and improving real-time agricultural processes. A critical element of the AOS observing strategy is to make extensive use of new passive and active sen-sors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires compre-hensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, at-mospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of the observing system simulation capabilities being developed for AOS, including global storm resolving nature runs, detailed instrument and retrieval simulators, as well as fast retrieval emulators for instrumenting climate models. This simulation environment, being developed under NASA’s open-source science initiative, will permit us to explore how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data, well before launch.

Arlindo da Silva↗
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 NTRS1999

Neural Networks Used to Compare Designed and Measured Time-Average Patterns

Electronic time-average holograms are convenient for comparing the measured vibration modes of fan blades with those calculated by finite-element models. At the NASA Lewis Research Center, neural networks recently were trained to perform what had been a simple visual comparison of the predictions of the design models with the measurements. Finite-element models were used to train neural networks to recognize damage and strain information encoded in subtle changes in the time-average patterns of cantilevers. But the design-grade finite element models were unable to train the neural networks to detect damage in complex blade shapes. The design-model-generated patterns simply did not agree well enough with the measured patterns. Instead, hybrid-training records, with measured time-average patterns as the input and model-generated strain information as the output, were used to effect successful training.

Decker, Arthur J.↗
NASA NTRS2000

A Flight Training Simulator for Instructing the Helicopter Autorotation Maneuver (Enhanced Version)

Autorotation is a maneuver that permits a safe helicopter landing when the engine loses power. A catastrophe may occur if the pilot's control inputs are incorrect, insufficient, excessive, or poorly timed. Due to the danger involved, full-touchdown autorotations are very rarely practiced. Because in-flight autorotation training is risky, time-consuming, and expensive, the objective of the project was to develop the first helicopter flight simulator expressly designed to train students in this critical maneuver. A central feature of the project was the inclusion of an enhanced version of the Pilot-Rotorcraft Intelligent Symbology Management Simulator (PRISMS), a virtual-reality system developed by Anacapa Sciences and Thought Wave. A task analysis was performed to identify the procedural steps in the autorotation, to inventory the information needed to support student task performance, to identify typical errors, and to structure the simulator's practice environment. The system provides immediate knowledge of results, extensive practice of perceptual-motor skills, part-task training, and augmented cueing in a realistic cockpit environment. Additional work, described in this report, extended the capabilities of the simulator in three areas: 1. Incorporation of visual training aids to assist the student in learning the proper appearance of the visual scene when the maneuver is being properly performed; 2. Introduction of the requirement to land at a particular spot, as opposed to the wide, flat open field initially used, and development of appropriate metrics of success; and 3. Inclusion of wind speed and wind direction settings (and random variability settings) to add a more realistic challenge in "hitting the spot."

Rogers, Steven P.↗
NASA NTRSDate not supplied

Crew Performance Support System to Aid in Anomaly Resolution: Concept of Operations

As missions progress into deep space, communication delays and disruptions will disenable the crew’s reliance on Earth experts. There are also limitations in the amount of data that can be downlinked to the ground. It is prudent to assume that critical, complex vehicle or habitat sub-systems will malfunction at a time when a lunar or Mars’ crew cannot rely on the Earth-Support team to detect, diagnose and resolve the problem and it is impractical to expect a small crew to step-in with the same level of expertise as 50+ authorities. The crew will need novel processes and advanced technological support to independently identify and resolve safety- and time-critical anomalies. That a self-reliant crew is unable to respond appropriately to time-critical anomalies is a significant risk to crew safety and mission success. This risk is driven by several factors; novel and unanticipated anomalies would not have been trained pre-flight, the crew could forget their pre-flight training or spaceflight stressors could impair the crew’s problem-solving ability. At last year’s IWS, Beard reported that a single spaceflight stressor (elevated CO2) could undermine the crew’s ability to independently respond to emergencies. Concept of Operations (ConOps) provide a common view of future system functions to all stakeholders. For the current project, a ConOps was developed that describes the operational processes, practices and capabilities needed by a crew of astronauts on deep space missions to autonomously respond to anticipated and unanticipated anomalies. It is crucial to recognize that, as of August 2018 existing technologies are unable to effectively support crew anomaly response to unanticipated events. “Intelligent technology” has not reached a maturity level that permits generalizing a solution to novel situations. For example, to train intelligent technology requires volumes of data that do not exist. The complexities involved in a manned mission to Mars cannot be compared to sending rovers to Mars using scripted software. This ConOps proposes a Crew Performance Support System (CPSS) that will push NASA and its industry partners toward what will be required for a safe and successful manned mission to Mars. Anomaly resolution during a deep space mission will take place within a dynamic, or changing, context. The figure to the left shows five broad contextual variables: the organizational culture, mission context, system characteristics, team characteristics and individual characteristics. The yellow arrow indicates that spaceflight and task-related stressors can affect system, team and individual crewmember characteristics and therefore anomaly response potential. The figure depicts a protective umbrella of Human-System Integration (HSI) principles that should be instituted during CPSS development including a balanced workload, shared situation awareness and building an appropriate level of trust in the automation. The figure also depicts two interrelated and cooperative components, an HSI Data System and other Enabling Capabilities will be required to support crew anomaly response and Earth-Support situation awareness. As we journey from ISS to Gateway to Mars, multiple, simultaneous and integrated research and development efforts (i.e., support systems co-evolution) must be implemented to meet the problem-solving challenges a self-reliant crew will face on a Mars’ mission. The crossovers between the capabilities are just as important as the discrete capabilities themselves. As the capabilities mature, the lines between the support subdomains will blur and an integrated system will emerge. The ConOps summarizes current knowledge about how highly trained people solve anomalies in safety- and time-critical situations, describes a group of capabilities that could help to reduce the extant risk and documents requirements levied on additional systems that provides critical inputs to the CPSS. Scenarios are used to promote a shared understanding of processes, practices and technological goals needed for safe and productive manned missions beyond LEO.

HSIA risk↗
NASA NTRS2017

Medical Support for ISS Crewmember Training in Star City, Russia

Medical support of spaceflight training operations across international lines is a unique circumstance with potential applications to other aerospace medicine support scenarios. KBRwyle's Star City Medical Support Group (SCMSG) has fulfilled this role since the Mir-Shuttle era, with extensive experience and updates to share with the greater AsMA community. OVERVIEW: The current Soyuz training flow for assigned ISS crewmembers takes place in Star City, Russia. Soyuz training flow involves numerous activities that pose potential physical and occupational risks to crewmembers, including centrifuge runs and pressurized suit simulations at ambient and hypobaric pressures. In addition, Star City is a relatively remote location in a host nation with variable access to reliable, Western-standard medical care. For these reasons, NASA's Human Health & Performance contract allocates full-time physician support to assigned ISS crewmembers training in Star City. The Star City physician also treats minor injuries and illnesses as needed for both long- and short-term NASA support personnel traveling in the area, while working to develop and maintain relationships with local health care resources in the event of more serious medical issues that cannot be treated on-site. The specifics of this unique scope of practice will be discussed. SIGNIFICANCE: ISS crewmembers training in Star City are at potential physical and occupational risk of trauma or dysbarism during nominal Soyuz training flow, requiring medical support from an on-duty aerospace medicine specialist. This support maintains human health and performance by preserving crewmember safety and well-being for mission success; sharing information regarding this operational model may contribute to advances in other areas of international, military, and civilian operational aerospace medicine.

Chough, Natacha↗
NASA NTRS2017

Medical Support for ISS Crewmember Training in Star City, Russia

Medical support of spaceflight training operations across international lines is a unique circumstance with potential applications to other aerospace medicine support scenarios. KBRwyle's Star City Medical Support Group (SCMSG) has fulfilled this role since the Mir-Shuttle era, with extensive experience and updates to share with the greater AsMA community. OVERVIEW: The current Soyuz training flow for assigned ISS crewmembers takes place in Star City, Russia. Soyuz training flow involves numerous activities that pose potential physical and occupational risks to crewmembers, including centrifuge runs and pressurized suit simulations at ambient and hypobaric pressures. In addition, Star City is a relatively remote location in a host nation with variable access to reliable, Western-standard medical care. For these reasons, NASA's Human Health & Performance contract allocates full-time physician support to assigned ISS crewmembers training in Star City. The Star City physician also treats minor injuries and illnesses as needed for both long- and short-term NASA support personnel traveling in the area, while working to develop and maintain relationships with local health care resources in the event of more serious medical issues that cannot be treated on-site. The specifics of this unique scope of practice will be discussed. SIGNIFICANCE: ISS crewmembers training in Star City are at potential physical and occupational risk of trauma or dysbarism during nominal Soyuz training flow, requiring medical support from an on-duty aerospace medicine specialist. This support maintains human health and performance by preserving crewmember safety and well-being for mission success; sharing information regarding this operational model may contribute to advances in other areas of international, military, and civilian operational aerospace medicine.

Chough, Natacha↗
NASA NTRS1994

The crash of P/Shoemaker-Levy 9 into Jupiter and its implications for comet bombardment on Earth

Periodic Comet Shoemaker/Levy 9 will impact Jupiter in late July 1994. The comet, which broke into more than 20 telescopically detectable fragments when it passed with the Roche lobe of Jupiter on July 8, 1992, is captured in a highly eccentric orbit about Jupiter. The 21 recognized nuclei will be spread out in a train of the order 7 x 10(exp 6) km long at the time of impact, and the impacts will be spread in time over about 5 1/2 days centered on about July 21.2 UT. In addition to the train of recognized bright nuclei, the comet consists of 'wings' of unresolved bodies that are the source of a very broad composite dust tail. The linear extent of the wings is about an order of magnitude greater than that of the train of recognized discrete nuclei. Collision of the wings will be spread in time over several months. Thus the impact of P/S-L 9 with Jupiter will be an event of appreciable duration.

Shoemaker, E. M.↗
NASA NTRS1991

Kappa Group: The initial guess. A proposal in response to a commercial air transportation study

Kappa Aerospace presents their Aeroworld Aircraft, the Initial Guess (IG). This aircraft is designed to generate profit in the market which is currently controlled by the train and boat industry. The main priority of the design team was to develop an extremely efficient aircraft that could be sold at a reasonable price. The IG offers a quick and safe alternative to the existing means of transportation at a competitive price. The cruise velocity of 28 ft/sec. allows all flights to be between 20 and 45 minutes, which is a remarkable savings in time compared to travel by boat or train. The IG is propelled by a single Astro-05 engine with a Zinger 10-6 propeller. The Astro-05 is not an extremely powerful engine; however, it provides enough thrust to meet the design and safety requirements. The major advantage of the Astro-05 is that it is the most efficient engine available. The fuel efficiency of the Astro-05 is what puts the aircraft ahead of the competition. The money saved on an efficient engine can be passed on as lower ticket prices or increased revenue. The IG has a payload of 56 passengers and a wingspan of 7 ft. The 7 ft. wingspan allows the aircraft to fit into the gates of all of the cities that are targeted. Future endeavors of Kappa Aerospace will include fitting a stretch version of the IG with a larger propulsion system. This derivative aircraft will be able to carry more passengers and will be placed on the routes which have the greatest demand for travel. The fuselage and empennage are made of a wooden truss configuration, while the wing is made of a rib/spare configuration. The stress carrying elements are made of spruce, the nonstress carrying elements are made of balsa. The wing is removable for easy access into the fuselage. The easy access to the batteries will keep maintenance costs down.

Source record↗
NASA NTRS2019

System and Method for Training of State-Classifiers

Method and systems are disclosed for training state-classifiers for classification of cognitive state. A set of multimodal signals indicating physiological responses of an operator are sampled over a time period. A depiction of operation by the operator during the time period is displayed. In response to user input selecting a cognitive state for a portion of the time period, the one or more state-classifiers are trained. In training the state-classifiers, the set of multimodal signals sampled in the portion of the time period are used as input to the one or more state-classifiers and the selected one of the set of cognitive states is used as a target result to be indicated by the one or more state-classifiers.

Stephens, Chad L.↗
NASA NTRS2012

Virtual Guidance Ultrasound: A Tool to Obtain Diagnostic Ultrasound for Remote Environments

Astronauts currently acquire ultrasound images on the International Space Station with the assistance of real-time remote guidance from an ultrasound expert in Mission Control. Remote guidance will not be feasible when significant communication delays exist during exploration missions beyond low-Earth orbit. For example, there may be as much as a 20- minute delay in communications between the Earth and Mars. Virtual-guidance, a pre-recorded audio-visual tutorial viewed in real-time, is a viable modality for minimally trained scanners to obtain diagnostically-adequate images of clinically relevant anatomical structures in an autonomous manner. METHODS: Inexperienced ultrasound operators were recruited to perform carotid artery (n = 10) and ophthalmic (n = 9) ultrasound examinations using virtual guidance as their only instructional tool. In the carotid group, each each untrained operator acquired two-dimensional, pulsed, and color Doppler of the carotid artery. In the ophthalmic group, operators acquired representative images of the anterior chamber of the eye, retina, optic nerve, and nerve sheath. Ultrasound image quality was evaluated by independent imaging experts. RESULTS: Eight of the 10 carotid studies were judged to be diagnostically adequate. With one exception the quality of all the ophthalmic images were adequate to excellent. CONCLUSION: Diagnostically-adequate carotid and ophthalmic ultrasound examinations can be obtained by untrained operators with instruction only from an audio/video tutorial viewed in real time while scanning. This form of quick-response-guidance, can be developed for other ultrasound examinations, represents an opportunity to acquire important medical and scientific information for NASA flight surgeons and researchers when trained medical personnel are not present. Further, virtual guidance will allow untrained personnel to autonomously obtain important medical information in remote locations on Earth where communication is difficult or absent.

Caine,Timothy L.↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
NASA NTRS2016

Compiling a Comprehensive EVA Training Dataset for NASA Astronauts

Training for a spacewalk or extravehicular activity (EVA) is considered a hazardous duty for NASA astronauts. This places astronauts at risk for decompression sickness as well as various musculoskeletal disorders from working in the spacesuit. As a result, the operational and research communities over the years have requested access to EVA training data to supplement their studies. The purpose of this paper is to document the comprehensive EVA training data set that was compiled from multiple sources by the Lifetime Surveillance of Astronaut Health (LSAH) epidemiologists to investigate musculoskeletal injuries. The EVA training dataset does not contain any medical data, rather it only documents when EVA training was performed, by whom and other details about the session. The first activities practicing EVA maneuvers in water were performed at the Neutral Buoyancy Simulator (NBS) at the Marshall Spaceflight Center in Huntsville, Alabama. This facility opened in 1967 and was used for EVA training until the early Space Shuttle program days. Although several photographs show astronauts performing EVA training in the NBS, records detailing who performed the training and the frequency of training are unavailable. Paper training records were stored within the NBS after it was designated as a National Historic Landmark in 1985 and closed in 1997, but significant resources would be needed to identify and secure these records, and at this time LSAH has not pursued acquisition of these early training records. Training in the NBS decreased when the Johnson Space Center in Houston, Texas, opened the Weightless Environment Training Facility (WETF) in 1980. Early training records from the WETF consist of 11 hand-written dive logbooks compiled by individual workers that were digitized at the request of LSAH. The WETF was integral in the training for Space Shuttle EVAs until its closure in 1998. The Neutral Buoyancy Laboratory (NBL) at the Sonny Carter Training Facility near JSC opened in March 1997 and is the current site for US EVA training. Other space agencies also have used water to simulate weightlessness and train for EVAs. Russia has a training facility similar to the NBL named the Hydro Lab. The Hydro Lab began operations at the Gagarin Cosmonaut Training Center (GCTC) in 1980 and has been used extensively to the present. Although a majority of training in the Hydro Lab uses the Russian Orlan suit, a small number of sessions have been conducted using a NASA suit. The Japanese Weightlessness Environment Test System (WETS) went into service at the Tsukuba Space Center in 1997 but was closed in 2011 due to extensive earthquake damage. Several sessions were performed using a NASA suit, but these sessions were short and considered "development" runs. LSAH has assembled records from the WETF, NBL and Hydro Lab. Recording of the EVA training data has changed considerably from 1967 to present. The goal of early record keeping was to track use of hardware components, and the person involved was treated as a suited operator, not as a focus of interest. Records from the past two decades are fairly precise with the person, date, suit type and size noted. On occasion the length of the session was listed, but this data is not included on all records. Records were merged from data sources and extensive cleaning of the records was required since the multiple sources frequently overlapped and duplicated records. To date the LSAH EVA training dataset includes over 12,500 EVA training sessions performed by NASA astronauts since 1981. The following variables are included for most records: Name, Sex, Event date, Event name, HUT type, HUT size, Facility, and Estimated run time. For a smaller subset of records, the following variables are available: Actual run time, Time inverted, and the suit components Waist bearing type, Shoulder harness, Shoulder pads, and Teflon inserts. The LSAH dataset is currently the most complete resource for data regarding EVA training sessions performed by NASA astronauts. However, it is not 100 percent complete since the WETS (Japan) and NBS (Marshall) training facility data were not included. This dataset has been compiled by LSAH to study the relationship of EVA training to musculoskeletal injuries but has many other non-medical applications. This dataset can be provided to other groups in order to respond to program and research questions with appropriate board approvals.

Laughlin, M. S.↗
NASA NTRS2009

Training Modalities to Increase Sensorimotor Adaptability

During the acute phase of adaptation to novel gravitational environments, sensorimotor disturbances have the potential to disrupt the ability of astronauts to perform required mission tasks. The goal of our current series of studies is develop a sensorimotor adaptability (SA) training program designed to facilitate recovery of functional capabilities when astronauts transition to different gravitational environments. The project has conducted a series of studies investigating the efficacy of treadmill training combined with a variety of sensory challenges (incongruent visual input, support surface instability) designed to increase adaptability. SA training using a treadmill combined with exposure to altered visual input was effective in producing increased adaptability in a more complex over-ground ambulatory task on an obstacle course. This confirms that for a complex task like walking, treadmill training contains enough of the critical features of overground walking to be an effective training modality. SA training can be optimized by using a periodized training schedule. Test sessions that each contain short-duration exposures to multiple perturbation stimuli allows subjects to acquire a greater ability to rapidly reorganize appropriate response strategies when encountering a novel sensory environment. Using a treadmill mounted on top of a six degree-of-freedom motion base platform we investigated locomotor training responses produced by subjects introduced to a dynamic walking surface combined with alterations in visual flow. Subjects who received this training had improved locomotor performance and faster reaction times when exposed to the novel sensory stimuli compared to control subjects. Results also demonstrate that individual sensory biases (i.e. increased visual dependency) can predict adaptive responses to novel sensory environments suggesting that individual training prescription can be developed to enhance adaptability. These data indicate that SA training can be effectively integrated with treadmill exercise and optimized to provide a unique system that combines multiple training requirements in a single countermeasure system. Learning Objectives: The development of a new countermeasure approach that enhances sensorimotor adaptability will be discussed.

Bloomberg, J. J.↗