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Programs which have been developed for training commercial airline pilots and flight crews are discussed. The concept of cockpit resource management and the concomitant issues of management techniques, interpersonal communication, psychological factors, and flight stress are addressed. Training devices and simulation techniques are reported.
This report describes the results of a workshop conducted at Goddard Space Flight Center (GSFC) to identify current and anticipated trends in human-computer interface technology that may influence the design or operation of a space station workstation. The workshop was attended by approximately 40 persons from government and academia who were selected for their expertise in some aspect of human-machine interaction research. The focus of the workshop was a 1 1/2 brainstorming/forecasting session in which the attendees were assigned to interdisciplinary working groups and instructed to develop predictions for each of the following technology areas: (1) user interface, (2) resource management, (3) control language, (4) data base systems, (5) automatic software development, (6) communications, (7) training, and (8) simulation. This report is significant in that it provides a unique perspective on workstation design for the space station. This perspective, which is characterized by a major emphasis on user requirements, should be most valuable to Phase B contractors involved in design development of the space station workstation. One of the more compelling results of the workshop is the recognition that no major technological breakthroughs are required to implement the current workstation concept. What is required is the creative application of existing knowledge and technology.
Night-vision goggles (NVGs) and, in particular, the advanced, helmet-mounted Aviators Night-Vision-Imaging System (ANVIS) allows helicopter pilots to perform low-level flight at night. It consists of light intensifier tubes which amplify low-intensity ambient illumination (star and moon light) and an optical system which together produce a bright image of the scene. However, these NVGs do not turn night into day, and, while they may often provide significant advantages over unaided night flight, they may also result in visual fatigue, high workload, and safety hazards. These problems reflect both system limitations and human-factors issues. A brief description of the technical characteristics of NVGs and of human night-vision capabilities is followed by a description and analysis of specific perceptual problems which occur with the use of NVGs in flight. Some of the issues addressed include: limitations imposed by a restricted field of view; problems related to binocular rivalry; the consequences of inappropriate focusing of the eye; the effects of ambient illumination levels and of various types of terrain on image quality; difficulties in distance and slope estimation; effects of dazzling; and visual fatigue and superimposed symbology. These issues are described and analyzed in terms of their possible consequences on helicopter pilot performance. The additional influence of individual differences among pilots is emphasized. Thermal imaging systems (forward looking infrared (FLIR)) are described briefly and compared to light intensifier systems (NVGs). Many of the phenomena which are described are not readily understood. More research is required to better understand the human-factors problems created by the use of NVGs and other night-vision aids, to enhance system design, and to improve training methods and simulation techniques.
An investigation was conducted at the NASA Langley Research Center's Aircraft Landing Dynamics Facility (ALDF) to define the post-tire failure drag characteristics of the Space Shuttle Orbiter main tire and wheel assembly. Skid tests on various materials were also conducted to define their friction and wear rate characteristics under higher speed and bearing pressures than any previous tests. The skid tests were conducted to support a feasibility study of adding a skid to the orbiter strut between the main tires to protect an intact tire from failure due to overload should one of the tires fail. Roll-on-rim tests were conducted to define the ability of a standard and a modified orbiter main wheel to roll without a tire. Results of the investigation are combined into a generic model of strut drag versus time under failure conditions for inclusion into rollout simulators used to train the shuttle astronauts.
This video shows astronauts Covey, Hilmers, and Hauck training in SES. It involves a simulated EVA rescue using the RMS. Training demonstrates the movement of a free-floating astronaut for grapple with the arm.
Astronaut Richards is shown during his ASCAN training, including weightless environment training facility (WETF) training and various simulations.
Training has been around as an informal process for countless years. Most higher order animals require some level of training in hunting, social skills, or other survival related skills to continue their existence beyond early infancy. Much of the training is accomplished through imitation, trial and error, and good luck. In some ways the essentials of training in aviation have not deviated from this original formula a great deal. One of the major changes in aviation and other technical areas is that more complex response chains based on a broader base of knowledge are now required. 'To certify' means many things according to the American Heritage dictionary of the English Language. These meanings range from 'to guarantee as meeting a standard' to 'to declare legally insane'. For this discussion, we will use the definition 'an action taken by some authoritative body that essentially guarantees that the instruction meets some defined standard'. In order to make this certification, the responsible body subjects the educational process, training, training device, or simulator to some type of examination to determine its adequacy or validity.
This paper describes how features from the field of Intelligent Tutoring Systems are applied to the Motor-Operated Valve (MOV) Advanced Technology Training System (ATTS). The MOV ATTS is a training system developed at Galaxy Scientific Corporation for the Central Research Institute of Electric Power Industry in Japan and the Electric Power Research Institute in the United States. The MOV ATTS combines traditional computer-based training approaches with system simulation, integrated expert systems, and student and expert modeling. The primary goal of the MOV ATTS is to reduce human errors that occur during MOV overhaul and repair. The MOV ATTS addresses this goal by providing basic operational information of the MOV, simulating MOV operation, providing troubleshooting practice of MOV failures, and tailoring this training to the needs of each individual student. The MOV ATTS integrates multiple expert models (functional and procedural) to provide advice and feedback to students. The integration also provides expert model validation support to developers. Student modeling is supported by two separate student models: one model registers and updates the student's current knowledge of basic MOV information, while another model logs the student's actions and errors during troubleshooting exercises. These two models are used to provide tailored feedback to the student during the MOV course.
Live footage of the STS-99 crew members shows Commander Kevin R. Kregel, Pilot Dominic L. Pudwill Gorie, Mission Specialists Janet L. Kavandi, Janice E. Voss, Mamoru Mohri, and Gerhard P.J. Thiele going through various training exercises. These exercises include Post Landing Egress, SRTM (Shuttle Radar Topography Mission) Deploy and Mapping Activities, HDTV (High Definition Television) Camera Training, and Ascent Simulation. Footage also includes the six-member crew participating in a photo session.
One of the biggest challenges for a pilot in the transition to a "glass" cockpit is understanding the Flight Management System (FMS). This is due to both the complex nature of the FMS and to the pilot-FMS interface. For these reasons, a large portion of transition training is devoted to the FMS. The intent of the current study was to examine the impact of the primary pilot-FMS interface, the Control Display Unit (CDU), on FMS training. The hypothesis of this study was that the interface design could have a significant impact on training. An FMS simulation was developed with two separate interfaces. One interface was similar to a current-generation design and the other was a multi-windows CDU based on graphical user interface techniques. For both application and evaluation reasons, constraints were applied to the graphical CDU design to maintain as much similarity as possible with the conventional CDU.
Two different space life sciences missions (SLS-1 and SLS-2) have demonstrated that the synapses of the hair cells of rat vestibular maculae increase significantly in microgravity. The results also indicate that macular synapses are sensitive to stress. These findings argue that vestibular maculae exhibit neuroplasticity to macroenvironmental and microenvironmental changes. This capability should be clinically relevant to rehabilitative training and/or pharmacological treatments for vestibular disease. The results of this ultrastructural research also demonstrated that type I and type II hair cells are integrated into the same neuronal circuitry. The findings were the basis for development of three-dimensional reconstruction software to learn details of macular wiring. This software, produced for scientific research, has now been adapted to reconstruct the face and skull directly from computerized tomography scans. In collaboration with craniofacial reconstructive surgeons at Stanford University Medical Center, an effort is under way to produce a virtual environment workbench for complex craniofacial surgery. When completed, the workbench will help surgeons train for and simulate surgery. The methods are patient specific. This research illustrates the value of basic research in leading to unanticipated medical applications.
Techniques to identify aircraft aerodynamic characteristics from flight measurements and compute corrections to an existing simulation model of a research aircraft were investigated. The purpose of the research was to develop a process enabling rapid automated updating of aircraft simulation models using flight data and apply this capability to all flight regimes, including flight envelope extremes. The process presented has the potential to improve the efficiency of envelope expansion flight testing, revision of control system properties, and the development of high-fidelity simulators for pilot training.
NASA's Space Launch System (SLS) has moved from design and manufacturing into testing and integration for its first flight as early as December 2019. In 2017, the NASA/industry team completed manufacturing of all major structural elements for the launch vehicle for Exploration Mission-I (EM-1 ). That work included shipping the first major flight hardware element to the launch site. The team processed all four RS-25 engines for stage integration, cast all 10 booster flight motor segments, and manufactured all five major sections of the core stage. The program also completed major structural work on the B-2 test stand at Stennis Space Center, which will be used for the core stage "green run" test; delivered the core stage and engine simulators used for training; and much of the transportation equipment for the core stage. Engineers completed structural testing on the upper stage/payload section of the vehicle as well as the engine section test article. In 2018, the program will deliver the Orion Stage Adapter (OSA) to Exploration Ground Systems (EGS) at KSC and send the test articles for the core stage liquid hydrogen tank, liquid oxygen tank, and intertank to NASA's Marshall Space Flight center for structural testing. Additionally, workers will begin the challenging process of integrating the major sections of the 212-foot EM-1 core stage. This work is focused on the initial Block 1 variant of SLS, capable of launching more than 70 metric tons (t) to low Earth orbit (LEO). However, work concurrently is underway on the Block lB variant, which will enable 105 t to LEO and more than 37t to trans-lunar injection (TLI). Block lB will be the workhorse vehicle of NASA's lunar exploration plans. As the needs of the nation's deep space exploration program grow, SLS performance is designed to evolve to a payload mass of 130 t to LEO and up to 45t to TLI. The advantages of this mass - as well as volume- are critical to the entire exploration architecture for deep space exploration. They translate to greater capability, greater infrastructure and operational simplicity, less overall mission risk, and opportunities to accomplish unprecedented exploration and discovery. This paper will discuss SLS progress to date and planned future work.
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.
This is one video in the "What's Behind This Door?" series. This series is designed to give the public a behind-the-scenes look at the various analogs and testing facilities used by NASA on our journey to return to the Moon. This particular episode explores JSC's PIT Lab, which uses a virtual reality environment for training subjects by simulating various environments such as the lunar surface.
Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.
This is an exciting time for cosmology with type Ia supernovae (SNe Ia). The recentlyconcluded Dark Energy Survey SN programme (DES-SN) has obtained the largest anddeepest high-redshift cosmological SN Ia sample, and the Vera Rubin Observatory isexpected to observe at least one order of magnitude more SNe Ia in the next decade.In both these experiments, only a limited fraction (.10 per cent) of the SNe can bespectroscopically classified. This leaves us with large ‘photometric’ SN samples, withthe potential for significant contamination by core-collapse SNe that may bias SN Iacosmological measurements. This thesis demonstrates how this contamination can bemodelled and accounted for in current and future cosmological analyses.First, I present state-of-the-art simulations of the SN universe. These are designedto accurately model the population of SNe Ia, peculiar SNe Ia and core-collapse SNe, aswell as their host galaxies. To improve the diversity and quality of the simulated corecollapseSNe, I build a new library of core-collapse SN templates using spectroscopicand photometric (optical and near-ultraviolet) data of 67 core-collapse SNe from theliterature. I account for our incomplete knowledge of core-collapse SN properties bygenerating a set of SN simulations (rather than a single one), each exploring differentmodelling choices and template libraries. I then characterise selection effects in theDES-SN survey and incorporate them in the simulations, thus obtaining a series ofDES-like simulated SN samples that can be compared to the observed DES-SN data.The agreement between the simulations and data is excellent across many observed SNproperties, including Hubble residuals. These simulations are the first to reproduce theobserved photometric SN and host galaxy properties in high-redshift surveys with no fine-tuning of the input parameters.I use my simulation framework to train and test the performance of SuperNNova,a photometric SN classifier based on recurrent neural networks. I explore differenttraining and validation strategies and show that, across all the DES-SN simulationstested, SuperNNova reduces core-collapse SN contamination to 0.8–3.5 per cent. Ithen show that biases due to contamination on the equation-of-state of dark energy,w, are < 0:008 when using our reference SuperNNova model. This compares to anexpected statistical uncertainty on w from the DES-SN sample of 0:039, thus showing that contamination is not a limiting systematic for the cosmological analysis of theDES-SN sample.The results presented in this thesis are the foundation of the DES SN Ia cosmologicalanalysis; they also provide important implications for the future of SN cosmology,as they demonstrate that contamination is not expected to significantly degrade thecosmological figure of merit of the Rubin SN Ia analysis.
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.