Simulator requirements deduced from comparisons of pilot's performance in ground simulators and in aircraft.
Simulator and flight test comparison of pilot performance to determine flight simulator requirements
PILOT PERFORMANCE↗
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EVA Planning: Using Neutral Buoyancy Laboratory (NBL) Training to Predict in-Flight Energy Expenditure
Metabolic rate (“met rate”) is the amount of energy expended over a period of time and is influenced by many factors including body composition, level of physical activity, resting metabolic rate, sex, age, and food intake. Met rate is measured during Extravehicular Activity (EVA) training at the Neutral Buoyancy Laboratory (NBL) and during in-flight EVAs through indirect calorimetry, calculating energy expenditure from respiratory measurements of O 2 consumption and/or CO 2 production. During Extravehicular Activity (EVA) planning, metabolic cost is important to consider and is used to inform EVA duration based on spacesuit consumables associated with life support systems. Currently, NBL and previous ISS EVA met rate data for specified crewmembers are utilized to predict in-flight EVA metabolic costs based on a proposed EVA timeline. Timeline data collected during training is used to relate met rates to specific EVA activities, which are in turn assigned to more generalized EVA task categories, categorizing by both task type and restraint type. EVA task categories include EVA Setup/Cleanup, Worksite Setup/Cleanup, Cable Routing, Bolts, Fluid Connectors, Electrical Connectors, R&R Work, Miscellaneous Work, Incapacitated Crew Rescue (being rescued or performing), Assisted Crew Rescue (being assisted or performing), and Translation. Restraint types consist of Free-Float, Body Restraint Tether (BRT), Articulating Portable Foot Restraint (APFR), and Space Station Remote Manipulator System (SSRMS). From a crewmember’s historical data, individualized 10th, 50th and 90th percentile met rate estimates are generated for each task category and used to estimate the proposed EVA timeline metabolic cost. In-flight metabolic data (“As-Executed”) from recent ISS US EVAs 85-88 (totaling eight EVA crewmember met rates) was compared with their predicted metabolic cost (“As-Planned”) to evaluate the accuracy of the current met rate estimation method. Across the four EVAs, As-Executed Cumulative EVA Total Metabolic Cost (M = 5893.78 BTU, SD = 816.60) was not significantly different compared to As-Planned Cumulative EVA Total Metabolic Cost (M = 6106.60 BTU, SD = 826.62; t(7) = 0.751 , p = .477). Though not a significant difference, generally, As-Planned total estimates were slightly higher than As-Executed total metabolic cost. Relative Error for Cumulative EVA Total Metabolic Cost ranged from -33% to 14.7%, depending on the crewmember and EVA. When comparing As-Planned to A-Executed EVA task categories for Bolts, Electrical Connectors, EVA Cleanup, EVA Setup, Miscellaneous Work, Translation, Worksite Cleanup, and Worksite Setup during these EVAs, no significant differences were observed, however, there was a significant difference in As-Planned (M = 783.71 BTU, SD = 408.88) compared to As-Executed (M = 608.87 BTU, SD = 425.29) metabolic cost for the task category of Repair-and-Replace (R&R) Work (t(17) = 3.21 , p = .005). Looking closer within the R&R Work task category, As-Executed R&R Work with Free-Float restraint type (M = 706.17 BTU, SD = 352.18) was significantly less than As-Planned R&R Work with Free-Float restraint type (M= 887.96 BTU, SD = 381.42; t(12) = 2.59, p < .024). As-Executed R&R Work with SSRMS Restraint type (M = 355.89 BTU, SD = 534.65) was not significantly different from As-Planned values (M = 512.69 BTU, SD = 383.34; t(4) = 1.88, p = 0.132). These findings suggest that the energy expended performing R&R Work (Free-Float) is lower in flight than predicted. Accurate predictions of the metabolic cost of EVA are essential for planning and executing successful ISS EVAs. Overall, the current met rate prediction method is similar to actual in-flight values, slightly erring on the side of overestimation. Future work includes analysis of more historical in-flight EVA data to increase the power of the analysis, evaluating the NBL-ISS met rate conversion factor between NBL and ISS tasks, as well as exploring methods of substitution when crewmembers are missing prior task category data.
Lauren Cox↗
ISS Payload Operations Training During the COVID-19 Pandemic: Impacts and Solutions
The onset of the COVID-19 pandemic brought a dramatic and rapid transformation to almost every aspect of humanity in 2020. The world’s space agencies and their missions were not immune to the wide-sweeping changes. One discipline principally affected was mission operations and the various groups supporting that function. Mission support teams, especially for complex and crewed missions like the International Space Station were forced to rapidly rethink how and where control center staff performed their vital work. Operations training – an essential element to mission ops, had unique hurdles to overcome. Operations training is responsible for preparing astronaut crews for their missions, providing training to flight controllers, as well as ensuring that new team members are ready to join their colleagues. Every element of training was impacted by COVID restrictions. From orientation and introductory classes for new controllers, simulations and advanced lessons, On the Job Training and final evaluations; all aspects faced new challenges. Trainers at NASA’s Marshall Space Flight Center in Huntsville, Alabama, were forced to become more efficient with trainees and resources to continue supporting payload operations. The pandemic started in the USA in March 2020. Immediately, NASA mandated that the support for ISS real-time operations was critical. As a result, physical access to key facilities was restricted. Trainers and trainees had to quickly shift to 100 percent remote learning. In the short term, this was not a problem. However, instructors discovered lessons that they were accustomed to delivering in a classroom environment did not translate to remote teaching. Another hurdle to operations training was the mandate that all simulations could only be held remotely. The logistics of even small simulations proved to be challenging due to IT restrictions and public internet limitations. With simulations essentially stopped, as well as the restrictions on most OJT, trainees were essentially stopped in their advancement towards certification. Once limitations were identified, trainers prioritized new options. Transitioning to all electronic learning materials was a relatively easy fix. Teaching to large groups took additional shifts in the training paradigm. Methods for preparing astronauts for their missions were revised. Simulation supervisors found more efficient techniques to provide realistic training experience. Communication and approvals from management was essential. In every case, the payload operations instructors found novel solutions to all functions listed. This paper will discuss the factors and solutions payloads operations trainers found to keep scientific research on the ISS flying forward to mission success.
ISS↗
Comments on preflight training for Gemini V.
Preflight planning and training stages for Gemini V, considering spacecraft test, mission simulators, planetarium, survival and parachute training, etc
MISSION PLANNING↗
Translating Research Into Airline Practice: Case Studies In Collaboration
Airline training departments are avid customers for research that will help them enhance the effectiveness of training and the safety of flight operations. However, various factors often make it difficult for training department managers to draw upon the large body of human factors research, e.g.: research may not address the specific questions facing the training departments, the research literature may not be in a form that training managers can readily interpret, researchers' recommendations may be too expensive or impractical to implement, etc. This panel will discuss ways in which researchers can work with training departments to design research and translate findings into products that airlines can use readily. This collaboration is most effective when it is an integral part of the study from its inception. To illustrate the process of collaboration we will use as a case study the recently completed LOFT (Line Oriented Flight Training) Debriefing research project. We will summarize the findings from that study and discuss how we translated those findings into two training tools: a manual on how to facilitate LOFT debriefings and a video that illustrates facilitation techniques in a realistically enacted debriefing. In some cases, instead of starting a new research project, training department needs can be addressed by reviewing the existing research literature and using expert opinion to develop products that specifically address those needs. To illustrate this approach we will discuss a recent informal working group of scientists and airline personnel that met to develop training material to enhance situation awareness. This group reviewed scientific literature and ASRS (Aviation Safety Reporting System) reports, analyzed contributing factors, and produced a model for managing situation awareness.
Dismukes, R. Key↗
Human Factors in Training
Future space missions will be significantly longer than current Shuttle missions and new systems will be more complex than current systems. Increasing communication delays between crews and Earth-based support means that astronauts need to be prepared to handle the unexpected on their own. As crews become more autonomous, their potential span of control and required expertise must grow to match their autonomy. It is not possible to train for every eventuality ahead of time on the ground, or to maintain trained skills across long intervals of disuse. To adequately prepare NASA personnel for these challenges, new training approaches, methodologies, and tools are required. This research project aims at developing these training capabilities. Training efforts in FY07 strongly focused on crew medical training, but also began exploring how Space Flight Resource Management training for Mission Operations Directorate (MOD) Flight Controllers could be integrated with systems training for optimal Mission Control Center operations. Beginning in January 2008, the training research effort will include team training prototypes and tools. The Training Task addresses Program risks that lie at the intersection of the following three risks identified by the Project: 1) Risk associated with poor task design; 2) Risk of error due to inadequate information; 3) Risk associated with reduced safety and efficiency due to poor human factors design.
Barshi, Immanuel↗
Airplane Upset Training Evaluation Report
Airplane upset accidents are a leading factor in hull losses and fatalities. This study compared five types of airplane-upset training. Each group was composed of eight, non-military pilots flying in their probationary year for airlines operating in the United States. The first group, 'No aero / no upset,' was made up of pilots without any airplane upset training or aerobatic flight experience; the second group, 'Aero/no upset,' of pilots without any airplane-upset training but with aerobatic experience; the third group, 'No aero/upset,' of pilots who had received airplane-upset training in both ground school and in the simulator; the fourth group, 'Aero/upset,' received the same training as Group Three but in addition had aerobatic flight experience; and the fifth group, 'In-flight' received in-flight airplane upset training using an instrumented in-flight simulator. Recovery performance indicated that clearly training works - specifically, all 40 pilots recovered from the windshear upset. However few pilots were trained or understood the use of bank to change the direction of the lift vector to recover from nose high upsets. Further, very few thought of, or used differential thrust to recover from rudder or aileron induced roll upsets. In addition, recovery from icing-induced stalls was inadequate.
Gawron, Valerie J.↗
Real Time Data System (RTDS)
Information is given in viewgraph form on the Real Time Data System (RTDS). The goals are to increase the quality of flight decision making, reduce and enhance flight controller training time, and serve as a near-operations technology test-bed. Information is given on the growth of RTDS; flight control disciplines; RTDS technology deployment in 1987-1989 and 1990-91; a functionality comparison of mainframes and workstations; and technology transfer activities.
Heindel, Troy A.↗
Fatigue‐Related Countermeasures for Long‐Duration Exploration Missions
The NASA Human Research Program's (HRP) Behavioral Health and Performance Element (BHP) supports and conducts research to mitigate deleterious outcomes related to fatigue, sleep loss, circadian desynchronization, and work overload. Objective evidence indicates that within the context of the International Space Station (ISS), sleep is reduced and there is circadian misalignment. Despite chronic sleep loss and high workloads; however, astronauts successfully complete their missions. Contributing to their success is not only the tremendous skills and capabilities of each astronaut, but also the collaborative team efforts amongst the crew, between flight and ground crews, and through real‐time care provided by medical personnel. It is anticipated that risks to human health and performance will increase in the context of exploration missions, where crewmembers will venture to deep space for extended durations and in small vehicles with limited communication with home. Hence, fatigue‐related countermeasures are being developed and/or validated that include unobtrusive monitoring technologies to detect fatigue‐related performance decrements, environmental countermeasures, and sleep education and training for flight and ground crews. Given that fatigue is an issue in current ISS missions, the BHP works collaboratively with Space Medicine operations to collect data in the operational environment, to validate fatigue-related countermeasures, and provide evidence‐based mitigations. Our presentation will summarize fatigue‐related operational research that is underway through NASA's BHP in partnership with its operational counterparts. Efforts include studies evaluating the effects of hypnotics, lighting protocols as countermeasures for circadian entrainment, and investigations involving education and training. This presentation will further identify, based on flight and terrestrial evidence, additional sleep and circadian countermeasures that may still be needed to support exploration missions. Lessons learned from transitioning research deliverables into ISS operations will also be discussed.
Whitmire, A.↗
Crew Medical Training to Progressively Enable EIMO
Background. Onboard medical capabilities have greatly expanded over the history of the US space program. Newly identified space-related medical conditions, technological advances, and longer mission durations have led to an increasing need for on-demand medical expertise. Lengthy communications delays, lack of resupply and evacuation opportunities on exploration-class missions place an ever-increasing burden on the crew to provide medical care. Having adequate knowledge, skills, and abilities (KSA) available is an essential component of successful Earth Independent Medical Operations (EIMO). Without appropriate crew training and KSA, cutting-edge medical equipment has little value. Presumably, the crew will include a qualified physician; however, if the physician is incapacitated, a non-physician crew medical officer (CMO) will be needed. While more crew time is needed for medical training, there will be concomitant increases in preflight training demands for vehicle system management, operations, science, and contingencies. In truly independent operations, onboard resources such as just-in-time training, mixed reality, decision support tools, and AI-enabled chatbot “consultants” will be needed to augment KSA. Overview. Because of crew time constraints, topical priorities must be determined for preflight training. Curricula should be developed that emphasize management of conditions with relatively high incidence and morbidity/mortality. Defining the required KSA levels to treat each condition is essential, but all crewmembers should have basic lifesaving skills. Procedural and diagnostic training on live patients and simulators should be prioritized over classroom lectures. Crews must be trained with onboard equipment, resources, mixed reality, and AI-based decision support tools. Mission simulations should include medical problems with/without ground support and with appropriate communication delays. Certification guidelines for each level of KSA must be established. Skills rapidly decay for non-physician CMO’s; both pre-flight and in-flight refresher training will be needed. During spaceflight just-in-time training, simulations, and onboard CME with crew physician can help retain skills. Discussion. Medical technology, simulation design, mixed reality, and AI are advancing at a dizzying rate. Recognizing the severe constraints on crew time, it is critical that astronaut training is highly efficient and adapted to keep pace with new innovations both pre-flight and during exploration missions. These challenges will be discussed during this panel session.
Jay Lemery↗
Lessons learned: DC-X
The DC-X was conceived and developed specifically to lay the ground work for significantly lowering the cost of space operations. The system design was based on an initial set of program goals and a finite, limited set of resources. The goal in its simplest terms was to demonstrate vertical landing after rotation of the vehicle from a nose-first to an engines-first altitude. Finite resources actually drove the selection of a robust design to reduce fabrication and preflight testing costs. The result was a system with a large amount of flexibility which allowed expansion of the test goals as the system, and test program, evolved. The use of the vehicle flight computer interfacing with the ground control system for flight crew training was also not an initial concept. However, by defining an architecture for the system control modes which allowed additions and modifications as learning progressed, the 6 DOF codes used for flight controls software development were transported to the operating system to be used in a simulated flight mode. Flight data reduction was also greatly improved as the program progressed, and the data needs and presentation were refined. The software, avionics hardware, and the FOCC system development proceeded ahead of the vehicle, primarily because most of the hardware elements were existing at the outset of the program. The Built-in-Test (BIT) for avionics and propulsion systems were adequate. Particularly the flight readiness system which verified the vehicle health after engine start and before throttle-up for flight.
Steinmeyer, D. A.↗
Spacelab cost reduction alternatives study. Volume 1: Executive summary
Alternative approaches to payload operations planning and control and flight crew training are defined for spacelab payloads with the goal of: lowering FY77 and FY 78 costs for new starts; lowering costs to achieve Spacelab operational capability; and minimizing the cost per Spacelab flight. These alternatives attempt to minimize duplication of hardware, software, and personnel, and the investment in supporting facility and equipment. Of particular importance is the possible reduction of equipment, software, and manpower resources such as comtational systems, trainers, and simulators.
Source record↗
Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks
Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.
Generative Machine Learning↗
Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks
Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.
Generative Machine Learning↗
The General-Use Nodal Network Solver (GUNNS) Modeling Package for Space Vehicle Flow System Simulation
The General-Use Nodal Network Solver (GUNNS) is a modeling software package that combines nodal analysis and the hydraulic-electric analogy to simulate fluid, electrical, and thermal flow systems. GUNNS is developed by L-3 Communications under the TS21 (Training Systems for the 21st Century) project for NASA Johnson Space Center (JSC), primarily for use in space vehicle training simulators at JSC. It has sufficient compactness and fidelity to model the fluid, electrical, and thermal aspects of space vehicles in real-time simulations running on commodity workstations, for vehicle crew and flight controller training. It has a reusable and flexible component and system design, and a Graphical User Interface (GUI), providing capability for rapid GUI-based simulator development, ease of maintenance, and associated cost savings. GUNNS is optimized for NASA's Trick simulation environment, but can be run independently of Trick.
Harvey, Jason↗
Flight Team Development in Support of LCROSS - A Class D Mission
The LCROSS (Lunar Crater Observation and Sensing Satellite) project presented a number of challenges to the preparation for mission operations. A class D mission under NASA s risk tolerance scale, LCROSS was governed by a $79 million cost cap and a 29 month schedule from "authority to proceed" to flight readiness. LCROSS was NASA Ames Research Center s flagship mission in its return to spacecraft flight operations after many years of pursuing other strategic goals. As such, ARC needed to restore and update its mission support infrastructure, and in parallel, the LCROSS project had to newly define operational practices and to select and train a flight team combining experienced operators and staff from other arenas of ARC research. This paper describes the LCROSS flight team development process, which deeply involved team members in spacecraft and ground system design, implementation and test; leveraged collaborations with strategic partners; and conducted extensive testing and rehearsals that scaled in realism and complexity in coordination with ground system and spacecraft development. As a testament to the approach, LCROSS successfully met its full mission objectives, despite many in-flight challenges, with its impact on the lunar south pole on October 9, 2009.
Tompkins, Paul D.↗
Astronauts' Performance: The Retention and Transfer of Training
The space environment imposes on the astronaut crew significant physiological, psycho-social, and cognitive loads that can not be replicated on the ground. These loads likely impact crew performance. To date, no systematic data collection has taken place to understand the effects of such loads on crew members’ ability to retain trained knowledge and skills, and to transfer such knowledge and skills to novel situations. The research described here was originally designed to be the first such study to systematically collect data on the effects of long duration space flight on training retention and transfer. Because current theories of retention and transfer are based on results obtained in university laboratories using undergraduate students as research participants, and because crew time in space is very expensive, this study was designed to compare the performance of 4 groups of subjects: crew members in space, crew members on the ground, crew-like subjects, and university undergraduate students. Results from the ground-phase of the study reported here demonstrate that crew members’ performance under cognitive load can not be predicted from the performance of university undergraduate students. It is still an open question the extent to which crew members’ cognitive performance in space can be predicted from the performance of crew members on the ground.
training↗
Houston, We Have a Problem Solving Model for Training
In late 2006, the Mission Operations Directorate (MOD) at NASA began looking at ways to make training more efficient for the flight controllers who support the International Space Station. The average certification times for flight controllers spanned from 18 months to three years and the MOD, responsible for technical training, was eager to develop creative solutions that would reduce the time to 12 months. Additionally, previously trained flight controllers sometimes participated in more than 50 very costly, eight-hour integrated simulations before becoming certified. New trainees needed to gain proficiency with far fewer lessons and training simulations than their predecessors. This poster presentation reviews the approach and the process that is currently in development to accomplish this goal.
Schmidt, Lacey↗