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NASA NTRS2009

Remotely Piloted Vehicles for Experimental Flight Control Testing

A successful flight test and training campaign of the NASA Flying Controls Testbed was conducted at Naval Outlying Field, Webster Field, MD during 2008. Both the prop and jet-powered versions of the subscale, remotely piloted testbeds were used to test representative experimental flight controllers. These testbeds were developed by the Subsonic Fixed Wing Project s emphasis on new flight test techniques. The Subsonic Fixed Wing Project is under the Fundamental Aeronautics Program of NASA's Aeronautics Research Mission Directorate (ARMD). The purpose of these testbeds is to quickly and inexpensively evaluate advanced concepts and experimental flight controls, with applications to adaptive control, system identification, novel control effectors, correlation of subscale flight tests with wind tunnel results, and autonomous operations. Flight tests and operator training were conducted during four separate series of tests during April, May, June and August 2008. Experimental controllers were engaged and disengaged during fully autonomous flight in the designated test area. Flaps and landing gear were deployed by commands from the ground control station as unanticipated disturbances. The flight tests were performed NASA personnel with support from the Maritime Unmanned Development and Operations (MUDO) team of the Naval Air Warfare Center, Aircraft Division

Motter, Mark A.↗
NASA NTRS1985

Eye-scan behavior in a flight simulation task as a function of level of training

The present study explored eye-scan behavior as a function of level of subject training. Oculometric (eye-scan) measures were recorded from each of ten subjects during training trials on a CRT-based flight simulation task. The task developed for the study incorporated subtasks representative of specific activities performed by pilots, but which could be performed at asymptotic levels within relatively short periods of training. Changes in eye-scan behavior were examined as initially untrained subjects developed skill in the task. Eye-scan predictors of performance on the task were found. Examination of eye-scan in proximity to selected task events revealed differences in the distribution of looks at the instruments as a function of level of training.

Comstock, J. R., Jr.↗
NASA NTRS2015

Transformational Phenomena as Predictors of Aircraft Accidents: What Goes Around Comes Around

Flight crew confusion, excessive stressworkload, and ineffective Crew Resource Management among other issues arising from dependence on automated systems on the flight deck have been identified as major causal factors in multiple fatal accidents, significant incidents and near misses. Many Loss of Control events have been traced to display of erroneous flight data and auto-flight system mode status to the crew. Safety was compromised by not having sufficiently obvious and unambiguous information available in order to permit quick diagnosis of aircraft status and then appropriate action to regain control of the aircraft energy state or trajectory. Continually evolving training and operational requirements related to aircraft automation have also presented critical challenges to commercial aviation. During times of high demand and low supply of experienced pilots (which is today the case in South-East Asia, for instance), basic training in manual flight may be minimal and as low as a few hundred flight hours on light aircraft before beginning training on advanced, highly automated aircraft. Predominant use of automation may cause aircrew trained in this way to have trouble performing traditionally simple operations such as manually switching to other runways or overriding the autopilot in tight situations. Inadequate crew training andor experience coupled with attempted manual flight in highly automated airplanes may more easily lead to loss of aircraft control in unusual situations such as high-altitude stallsupsets, traffic avoidance or maneuvering. Loss of basic piloting skills through increasing dependence on automation may exacerbate this problem. Finally, design changes by nature take a long time and are very costly. Incorporating novel automated functionality into new aircraft designs is technically feasible and desirable. However, it may take many years for these changes to have a significant impact on tomorrows fleet, given the time it takes to develop a new aircraft and for these aircraft to become a significant fraction of the fleet. This paper will review and summarize the findings and recommendations from a 2004 study of the topic, Increasing reliance on flight deck automation conducted by the Future Aviation Safety Team at the behest of the Joint Safety Strategy Initiative (JSSI) within the Joint Aviation Authorities (JAA) organization that existed at the time. Although this study was conducted more than ten years ago, its findings and recommendations are as relevant now as then and into the future.

safety↗
NASA NTRSDate not supplied

Virtual Fit Assessment: Validation using Historical Spacesuit Fit Data

Virtual fit tests using 3D body scans have provided a cost-effective means to predictively assess spacesuit fit for the current and future astronaut population. However, fit is a complex issue influenced by physical interferences, subjective preference, and many other factors. Fit can substantially differ between the type of hardware, environmental conditions, and tasks being performed. Namely, fit changes across different contexts, such as 3D printed mockup evaluations, pressurized one-g suited test events, neutral buoyancy and other training events, and flight extravehicular activities (EVA). These challenges make it difficult to validate virtual fit frameworks to physical suit sizing. This study proposes a new method of validation using the wealth of historically archived suit fit data. Physically Assessed Fit (PAF) data was assessed from astronauts and test volunteers who wore the legacy Extravehicular Mobility Unit (EMU). The data was collected during the past decades at NASA and considered to be most reliable and dependable. Each participant was 3D scanned for body shape and spacesuit-critical dimensions were measured per NASA guidelines. The participants’ size preference for hard upper torso (HUT) assembly was retrieved from their pressurized suit fit check records. Fit assessments were also updated after one-g EVA training, neutral buoyancy training, and EVA flights. Virtually Assessed Fit (VAF) was done by overlaying the CAD model of a HUT with the 3D body scans of the PAF participants. Each body scan was iteratively adjusted for the position inside the HUT to minimize the suit-to-body contact interference while satisfying a set of prescribed requirements. Then the residual contact area, depth, and volume were quantified as VAF metrics. This process was repeated for the different HUT sizes, including medium, large, and extra-large. Statistical modeling is currently in progress and will be presented at the conference. A statistical classifier will be developed to predict a fit probability for different HUT sizes as a function of the corresponding person’s VAF metrics. The HUT size with the highest fit probability will constitute the most likely size selection for the person, and the prediction will be compared against the corresponding PAF data. The participant data will be randomly pre-grouped into either a model development or validation subset. While the model development subset will be used to build the probability model, the validation subset will be used to assess the model accuracy. Also, the locations and magnitudes of suit-body-contacts will be estimated from VAF. This information can identify the critical suit geometry and body shape features that influence suit fit. The variations found in PAF size selections by subjects with similar VAF metrics will allow for investigations into subjective preferences, for example, tight versus loose fit. Overall, this study is expected to provide a structured validation of a virtual suit fit framework, which has not been possible in the past. The outcome can also provide useful insights and potential limitations for interpreting virtual fit tests for future spacesuit designs and population accommodation.

Han Kim↗
NASA NTRS2009

Use of DSC and DMA to Study Rubber Crystallization as a Possible Cause for a Tear in a Neoprene Glove Used in a Space Shuttle Pressurized Astronaut Suit

The Advanced Crew Escape Suit (ACES) is a pressurized suit normally worn by astronauts during launch and landing phases of Space Shuttle operations. In 2008, a large tear (0.5 -1 in. long, between the pinky and ring finger) in the ACES left-hand glove made of neoprene latex rubber was found during training for Shuttle flight STS-124. An investigation to help determine the cause(s) of the glove tear was headed by the NASA Johnson Space Center (JSC) in Houston, Texas. Efforts at JSC to reproduce the actual glove tear pattern by cutting/tearing or rupturing were unsuccessful. Chemical and material property data from JSC such as GC-MS, FTIR, DSC and TGA mostly showed little differences between samples from the torn and control gloves. One possible cause for the glove tear could be a wedding ring/band worn by a male astronaut. Even with a smooth edge, such a ring could scratch the material and initiate the tear observed in the left-hand glove. A decision was later made by JSC to not allow the wearing of such a ring during training or actual flight. Another possible cause for the ACES glove tear is crystallinity induced by strain in the neoprene rubber over a long period of time and use. Neoprene is one several elastomeric materials known to be susceptible to crystallization, and such a process is accelerated with exposure of the material to cold temperatures plus strain. When the temperature is lowered below room temperature, researchers have shown that neoprene crystallization may be maintained at temperatures as high as 45-50 F, with a maximum crystallization rate near 20-25 F (1). A convenient conditioning temperature for inducing neoprene crystallization is a typical freezer that is held near 0 F. For work at the NASA Marshall Space Flight Center (MSFC), samples were cut from several areas/locations (pinky/ring finger crotch, index finger and palm) on each of two pairs of unstrained ACES gloves for DSC and DMA thermal analysis testing. The samples were conditioned in a freezer for various times up to about 14 days. Some rectangular conditioned samples were unstrained, while most were subjected to strains up to 250% with the aid of two slotted aluminum blocks and two aluminum clamps per sample. Trends were observed to correlate DSC data (heat of fusion) and DMA data (linear CTE and stress for iso-strain testing) with: (a) sample location on each glove; and (b) level of strain during conditioning. Control samples cut as is from each glove location were also tested by DSC and DMA.

Wingard, Doug↗
NASA NTRS2020

Monitoring for Flight Path Management: Inputs to Pilot Monitoring Training

The commercial aviation industry world-wide has identified a need for improved pilot monitoring (e.g., ICAO, 2016). More specifically, aviation safety data indicate that failures in pilots' monitoring for flight path management (FPM) have contributed to a range of undesired outcomes: accidents, major upsets, and non-compliance with ATC guidance. The FAA has further stated that these types of FPM failures are likely to worsen with the increasingly complex air traffic control systems and FPM concepts proposed for NextGen (https://www.faa.gov/nextgen/what_is_nextgen/) operations (e.g., see Hah et al., 2017). An important element of this additional complexity will be the introduction of new automation or artificial intelligence that is intended to work with the flight crew but can add additional monitoring burdens. One potential mitigation for this situation is to enhance pilot training for effective monitoring. NASA Ames Research Center was asked to identify and evaluate training approaches that have the potential to enhance pilots' ability to effectively monitor for FPM (with the result of improved awareness). The focus of this work is to identify, develop or validate training guidance to improve pilot monitoring/awareness regarding FPM and mitigate the recent trend of accidents and incidents, especially Loss of Control (LOC) events. The result of this work should be guidance for FAA training and standards organizations—such as the Air Carrier Training Aviation Rulemaking Committee (ACT ARC)—to aid in reducing the risk of incidents and accidents due to inadequate pilot monitoring/awareness.

Feary, Mike↗
NASA NTRS1993

Proceedings of the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology, Volume 1

These proceedings are organized in the same manner as the conference's contributed sessions, with the papers grouped by topic area. These areas are as follows: VE (virtual environment) training for Space Flight, Virtual Environment Hardware, Knowledge Aquisition for ICAT (Intelligent Computer-Aided Training) & VE, Multimedia in ICAT Systems, VE in Training & Education (1 & 2), Virtual Environment Software (1 & 2), Models in ICAT systems, ICAT Commercial Applications, ICAT Architectures & Authoring Systems, ICAT Education & Medical Applications, Assessing VE for Training, VE & Human Systems (1 & 2), ICAT Theory & Natural Language, ICAT Applications in the Military, VE Applications in Engineering, Knowledge Acquisition for ICAT, and ICAT Applications in Aerospace.

Hyde, Patricia R.↗
NASA NTRS2000

Bion 11 mission: primate experiments

A summary is provided of the major operations required to conduct the wide range of primate experiments on the Bion 11 mission, which flew for 14 days beginning December 24, 1996. Information is given on preflight preparations, including flight candidate selection and training; attachment and implantation of bioinstrumentation; flight and ground experiment designs; onboard life support and test systems; ground and flight health monitoring; flight monkey selection and transport to the launch site; inflight procedures and data collection; postflight examinations and experiments; and assessment of results.

NASA Discipline Musculoskeletal↗
NASA NTRS2005

Measurements of Load Train Motion on a Stratospheric Balloon Flight

Attitude measurements using gyros and magnetometers placed on a stratospheric balloon during a non-pointed test flight were used to observe the natural azimuth and elevation motions of a balloon/load train/gondola at an altitude of 36 km over a total flight time of 400 minutes. Time traces of the entire flight are presented. This flight, conducted under nominal atmospheric conditions, had significant motion about the azimuth. Some discussion on balloon disturbances is also included.

Gruner, Timothy D.↗
NASA NTRS2018

Training for Mars

Spaceflight training for Apollo era and Space Shuttle missions was designed to train crew on specific tasks for their short duration missions. The training mottos included "practice, practice, practice" and "hammering it in." However, training for longer duration ISS (International Space Station) missions has been more challenging. There is clear documentation that tasks and skills trained pre-flight are not all retained throughout the mission, and this lack of training retention is augmented with real-time ground support. NASA does not currently have a training design that supports semi-autonomous to autonomous mission operations. The current design of a manned Mars mission calls for a 32-month long mission and asynchronous communication involving long delays.

Barshi, Immanuel↗
NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗
NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗
NASA NTRS2011

Next Steps in the Evolution of Human Spaceflight Training

Train before you fly has always been a watchword at NASA, and consequently, NASA has been conducting training for human spaceflight missions for longer than it has been involved in the actual conduct of human spaceflight missions. Throughout that time, NASA s approach to human spaceflight training has continuously evolved to keep pace with the technology of the modern world, but the approach to training itself has not changed significantly. Today, there are more tools and technologies that enable learning than ever before. This paper intends to review the challenges of human spaceflight training and how modern technology and an updated approach could improve that training. The Spaceflight Training Management Office (DA7) within the Mission Operations Directorate (MOD) has been investigating the current training of instructors, flight controllers and astronauts in order to identify where a new approach to training and training management may be necessary to improve the efficacy of the training provided. Through this investigation, the DA7 team has identified potential areas of improvement within International Space Station (ISS) training in a wide range of areas, including the delivery of training, the structure of the training program, the concept of what is considered training, and the management of that training. The ISS is an operational program with an established training paradigm. As such, the implementation of these concepts will be met with several challenges that may prevent or preclude them from being adopted. These challenges include demonstrating return-on-investment (ROI) and overcoming cultural or technological obstacles. This report will delve into the possible improvement areas for training, the future training concepts that are being considered, and the challenges associated with implementation. The paper will include concepts for utilization of Web 2.0 technologies, electronic learning, digital media, and other technologies in the development, management, and conduct of human spaceflight training.

Balmain, Clint↗