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NASA NTRS2014

Transfer-of-Training Research at NASA Ames Research Center

In this presentation, I will first give an overview of some of the research being performed at the Human Systems Integration Division of NASAs Ames Research Center. In the second part of the presentation, I will discuss two studies that investigate the effects of simulator motion on pilot transfer of training. In a study funded by the Federal Aviation Administration, task performance is used to investigate the effects of motion on the training of 4 challenging tasks in a transport category aircraft. In addition, the effectiveness of recently proposed objective motion cueing criteria for training simulators are evaluated. The second study, part of the NASA Aeronautics Research Program, focuses on optimizing simulator motion for maximum transfer of stall recovery training and utilizes a cybernetic approach to measure transfer of training.

Zaal, Peter M. T.↗
NASA NTRS1983

The effectiveness of an oculometer training tape on pilot and copilot trainees in a commercial flight training program

A study was designed to evaluate the effectiveness of a video tape detailing the various aspects of instrument scanning by experienced pilots on performance by pilots and copilots undergoing flight training in a Boeing 737 flight simulator. The performance ratings by instructor pilots (IP's) and self-reported instrument scan behavior by trainees were compared with those of a control group. The results indicated that the training tape had little or no effect on performance by trainees in the experimental group. Feedback from the IP's and trainees suggested that a feedback strategy providing each trainee's individual instrument scan behavior might be more beneficial in flight training than the general instructional strategy of the oculometer training tape. Flight training personnel and trainees' reports of performance decrements on or around the third day of flight simulator training were investigated. The IP's performance ratings of 27 pilot and copilot trainees failed to reveal a systematic performance decrement; however, feedback from the trainees revealed that their own attribution of performance decrements was associated with the order in which their training occurred within a session. Further research was suggested.

Jones, D. H.↗
NASA NTRS2002

The Aircraft Simulation Role in Improving Flight Safety Through Control Room Training

NASA Dryden Flight Research Center uses its six-degrees-of-freedom (6-DOF) fixed-base simulations for mission control room training to improve flight safety and operations. This concept is applied to numerous flight projects such as the F-18 High Alpha Research Vehicle (HARV), the F-15 Intelligent Flight Control System (IFCS), the X-38 Actuator Control Test (XACT), and X-43A (Hyper-X). The Dryden 6-DOF simulations are typically used through various stages of a project, from design to ground tests. The roles of these simulations have expanded to support control room training, reinforcing flight safety by building control room staff proficiency. Real-time telemetry, radar, and video data are generated from flight vehicle simulation models. These data are used to drive the control room displays. Nominal static values are used to complete information where appropriate. Audio communication is also an integral part of training sessions. This simulation capability is used to train control room personnel and flight crew for nominal missions and emergency situations. Such training sessions are also opportunities to refine flight cards and control room display pages, exercise emergency procedures, and practice control room setup for the day of flight. This paper describes this technology as it is used in the X-43A and F-15 IFCS and XACT projects.

Shy, Karla S.↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRS1984

Simulation

The application of flight simulation in regional airline training programs is discussed. Specifically, the use of simulation in cockpit resources management training (CRMT) is investigated. The availability of simulation resources is explored and the simulator disadvantages and advantages are cited. Problems with simulator specification, procurement, validation and use that have plagued the major air carriers over several decades are addressed.

Foster, F.↗
NASA NTRS2011

Smackdown: Adventures in Simulation Standards and Interoperability

The paucity of existing employer-driven simulation education and the need for workers broadly trained in Modeling & Simulation (M&S) poses a critical need that the simulation community as a whole must address. This paper will describe how this need became an impetus for a new inter-university activity that allows students to learn about simulation by doing it. The event, called Smackdown, was demonstrated for the first time in April at the Spring Simulation Multi-conference. Smackdown is an adventure in international cooperation. Students and faculty took part from the US and Europe supported by IEEE/SISO standards, industry software and National Aeronautics and Space Administration (NASA) content of are supply mission to the Moon. The developers see Smackdown providing all participants with a memorable, interactive, problem-solving experience, which can contribute, importantly to the workforce of the future. This is part of the larger need to increase undergraduate education in simulation and could be a prime candidate for senior design projects.

Elfrey, Priscilla R.↗
NASA NTRS2016

The Trick Simulation Toolkit: A NASA/Opensource Framework for Running Time Based Physics Models

The Trick Simulation Toolkit is a simulation development environment used to create high fidelity training and engineering simulations at the NASA Johnson Space Center and many other NASA facilities. Its purpose is to generate a simulation executable from a collection of user-supplied models and a simulation definition file. For each Trick-based simulation, Trick automatically provides job scheduling, numerical integration, the ability to write and restore human readable checkpoints, data recording, interactive variable manipulation, a run-time interpreter, and many other commonly needed capabilities. This allows simulation developers to concentrate on their domain expertise and the algorithms and equations of their models. Also included in Trick are tools for plotting recorded data and various other supporting utilities and libraries. Trick is written in C/C++ and Java and supports both Linux and MacOSX computer operating systems. This paper describes Trick's design and use at NASA Johnson Space Center.

Penn, John M.↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRS1980

British Airways' pre-command training program

Classroom, flight simulator, and in-flight sessions of an airline pilot training program are briefly described. Factors discussed include initial command potential assessment, precommand airline management studies course, precommand course, and command course.

Holdstock, L. F. J.↗
NASA NTRS2007

Use of Combined A-Train Observations to Validate GEOS Model Simulated Dust Distributions During NAMMA

During August 2006, the NASA African Multidisciplinary Analyses Mission (NAMMA) field experiment was conducted to characterize the structure of African Easterly Waves and their evolution into tropical storms. Mineral dust aerosols affect tropical storm development, although their exact role remains to be understood. To better understand the role of dust on tropical cyclogenesis, we have implemented a dust source, transport, and optical model in the NASA Goddard Earth Observing System (GEOS) atmospheric general circulation model and data assimilation system. Our dust source scheme is more physically based scheme than previous incarnations of the model, and we introduce improved dust optical and microphysical processes through inclusion of a detailed microphysical scheme. Here we use A-Train observations from MODIS, OMI, and CALIPSO with NAMMA DC-8 flight data to evaluate the simulated dust distributions and microphysical properties. Our goal is to synthesize the multi-spectral observations from the A-Train sensors to arrive at a consistent set of optical properties for the dust aerosols suitable for direct forcing calculations.

Nowottnick, E.↗
NASA NTRS2005

Correlating Computed and Flight Instructor Assessments of Straight-In Landing Approaches by Novice Pilots on a Flight Simulator

The rising cost of flight training and the low cost of powerful computers have resulted in increasing use of PC-based flight simulators. This has prompted FAA standards regulating such use and allowing aspects of training on simulators meeting these standards to be substituted for flight time. However, the FAA regulations require an authorized flight instructor as part of the training environment. Thus, while costs associated with flight time have been reduced, the cost associated with the need for a flight instructor still remains. The obvious area of research, therefore, has been to develop intelligent simulators. However, the two main challenges of such attempts have been training strategies and assessment. The research reported in this paper was conducted to evaluate various performance metrics of a straight-in landing approach by 33 novice pilots flying a light single engine aircraft simulation. These metrics were compared to assessments of these flights by two flight instructors to establish a correlation between the two techniques in an attempt to determine a composite performance metric for this flight maneuver.

Heath, Bruce E.↗
NASA NTRS1981

The role of the research simulator in the systems development of rotorcraft

Over the last 20 years, flight simulators became widely accepted as training tools. Moreover, research simulators were used extensively by the fixed-wing industry: in the design, testing, and certification of new aircraft. The rotorcraft industry, however, was slow to use man-in-the-loop simulation to solve its design problems, primarily because of the difficulty of modeling complex rotorcraft for realtime simulation and because of the need for a wide-angle visual system for low-level flight. A joint U.S. Army and NASA program was initiated to provide this simulation capability for exploitation by both government and industry. The potential application of the research simulator to future rotorcraft systems design, development, product improvement evaluations, and safety analysis is discussed.

Statler, I. C.↗
NASA NTRS1988

Launch team training system

A new approach to the training, certification, recertification, and proficiency maintenance of the Shuttle launch team is proposed. Previous training approaches are first reviewed. Short term program goals include expanding current training methods, improving the existing simulation capability, and scheduling training exercises with the same priority as hardware tests. Long-term goals include developing user requirements which would take advantage of state-of-the-art tools and techniques. Training requirements for the different groups of people to be trained are identified, and future goals are outlined.

Webb, J. T.↗
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 NTRS2017

Time-Varying Loads of Co-Axial Rotor Blade Crossings

The blade crossing event of a coaxial counter-rotating rotor is a potential source of noise and impulsive blade loads. Blade crossings occur many times during each rotor revolution. In previous research by the authors, this phenomenon was analyzed by simulating two airfoils passing each other at specified speeds and vertical separation distances, using the compressible Navier-Stokes solver OVERFLOW. The simulations explored mutual aerodynamic interactions associated with thickness, circulation, and compressibility effects. Results revealed the complex nature of the aerodynamic impulses generated by upperlower airfoil interactions. In this paper, the coaxial rotor system is simulated using two trains of airfoils, vertically offset, and traveling in opposite directions. The simulation represents multiple blade crossings in a rotor revolution by specifying horizontal distances between each airfoil in the train based on the circumferential distance between blade tips. The shed vorticity from prior crossing events will affect each pair of upperlower airfoils. The aerodynamic loads on the airfoil and flow field characteristics are computed before, at, and after each airfoil crossing. Results from the multiple-airfoil simulation show noticeable changes in the airfoil aerodynamics by introducing additional fluctuation in the aerodynamic time history.

Time-Varying Loads↗
NASA NTRS2022

Reduced-Gravity Simulator for Field Environments - Drone Augmented System

Astronauts need to go through extensive training on Earth before heading into space, and the more accurate the analogue, the better prepared they will be. NASA currently trains astronauts in simulated lower gravity environments in either the Neutral Buoyancy Laboratory or the Active Response Gravity Offload System (ARGOS), but neither of these systems allow for testing outdoors in field environments. NASA, in partnership with Aquiline Drones, mechanical engineering, biomedical engineering, and the Krenicki Arts and Engineering Institute, are working to develop a gravity offload device compatible with testing in field environments such as Desert Research and Technology Studies (Desert RATS). This report covers the technical requirements of the gravity offload device, the design choices and justifications, options for scalability, current progress, and future goals. Two separate teams were formed to respond to NASA’s solicitation, and this report covers the work of the weather balloon and drone side of the project. Using a weather balloon to passively offload the user’s weight and a drone to actively respond to changes using a series of sensors, this system should create a consistent and well balanced offloading force. The weather balloon and drone system is connected to a harness system to comfortably lift the user, offload at their center of gravity, and provide attachment points for some of the necessary sensors. Final testing proved the comfort of the harness, mobility of the user, and passive stability of the balloon offload to exceed all expectations of the team. The current system is a scaled model of what NASA would be able to implement with a maximum offload of 25 pounds. The ability to scale up to NASA’s desired value of 100 pounds is easily achievable with linear scaling of the current model. All systems should work similarly on scaled versions, but minor tweaking and enhanced controls would be needed for optimal performance.

Jason Lee↗
NASA NTRS1986

A technique for incorporating the NASA spacelab payload dedicated experiment processor software into the simulation system for the payload crew training complex

The feasibility of some off-the-shelf microprocessors and state-of-art software is assessed (1) as a development system for the principle investigator (pi) in the design of the experiment model, (2) as an example of available technology application for future PI's experiments, (3) as a system capable of being interactive in the PCTC's simulation of the dedicated experiment processor (DEP), preferably by bringing the PI's DEP software directly into the simulation model, (4) as a system having bus compatibility with host VAX simulation computers, (5) as a system readily interfaced with mock-up panels and information displays, and (6) as a functional system for post mission data analysis.

Bremmer, D. A.↗
NASA NTRSDate not supplied

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↗