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Generalized outlines are presented for simulation in human factors research. Recent trends in aeronautical simulation are given. Some criteria for effective training devices are also given. Full system/full mission simulation in aviation and in space human factors research is presented.
A large-eddy simulation / Reynolds-averaged Navier-Stokes (LES/RANS) methodology is used to simulate premixed ethylene-air combustion in a model scramjet designed for dual mode operation and equipped with a cavity for flameholding. A 22-species reduced mechanism for ethylene-air combustion is employed, and the calculations are performed on a mesh containing 93 million cells. Fuel plumes injected at the isolator entrance are processed by the isolator shock train, yielding a premixed fuel-air mixture at an equivalence ratio of 0.42 at the cavity entrance plane. A premixed flame is anchored within the cavity and propagates toward the opposite wall. Near complete combustion of ethylene is obtained. The combustor is highly dynamic, exhibiting a large-scale oscillation in global heat release and mass flow rate with a period of about 2.8 ms. Maximum heat release occurs when the flame front reaches its most downstream extent, as the flame surface area is larger. Minimum heat release is associated with flame propagation toward the cavity and occurs through a reduction in core flow velocity that is correlated with an upstream movement of the shock train. Reasonable agreement between simulation results and available wall pressure, particle image velocimetry, and OH-PLIF data is obtained, but it is not yet clear whether the system-level oscillations seen in the calculations are actually present in the experiment.
NASA astronaut training program including Manned Spacecraft Center simulation operations
Activities this summer consisted of two projects that involved computer simulation of bioregenerative life support systems for space habitats. Students in the Space Life Science Training Program (SLSTP) used the simulation, space station, to learn about relationships between humans, fish, plants, and microorganisms in a closed environment. One student complete a six week project to modify the simulation by converting the microbes from anaerobic to aerobic, and then balancing the simulation's life support system. A detailed computer simulation of a closed lunar station using bioregenerative life support was attempted, but there was not enough known about system restraints and constants in plant growth, bioreactor design for space habitats and food preparation to develop an integrated model with any confidence. Instead of a completed detailed model with broad assumptions concerning the unknown system parameters, a framework for an integrated model was outlined and work begun on plant and bioreactor simulations. The NASA sponsors and the summer Fell were satisfied with the progress made during the 10 weeks, and we have planned future cooperative work.
Training includes both SFRM-dedicated lessons and SFRM training embedded into technical lessons. Goal is to reduce certification times by 50% and integrated simulations by 75-90%. SFRM is practiced, evaluated and debriefed in part task trainers and full-task simulation lessons. SFRM model and training are constantly being evaluated against student/management feedback, best practices from industry/ military, and latest research.
In this work, we augmented existing methods for estimating pilot workload ratings with deep neural networks trained using data from simulated flight tests in the Vertical Motion Simulator (VMS). We used an existing method, Spare Capacity Operations Estimator (SCOPE), along with a recurrent neural network and conducted comparison studies between the two methods individually, and when used together. We found that using both methods together can improve the result over using either approach alone. In our first test case, we achieved an improved linear correlation coefficient of 0.409 over that of SCOPE alone at 0.352 on the training dataset. Through cross validation, we also found that the results may be dependent on the split of training vs. validation data, and that further investigation should be conducted to understand what additional inputs to the neural network model should be made.
A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The procedure yielded a modified design that improves the aerodynamic performance through small changes to the reference design geometry. These results demonstrate the capabilities of the neural net-based design procedure, and also show the advantages of including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.
The Medical Operational Support Team (MOST) has been tasked by the Space and Life Sciences Directorate (SLSD) at the NASA Johnson Space Center (JSC) to integrate medical simulation into 1) medical training for ground and flight crews and into 2) evaluations of medical procedures and equipment for the International Space Station (ISS). To do this, the MOST requires patient models that represent the physiological changes observed during spaceflight. Despite the presence of physiological data collected during spaceflight, there is no defined set of parameters that illustrate or mimic a 'space normal' patient. Methods: The MOST culled space-relevant medical literature and data from clinical studies performed in microgravity environments. The areas of focus for data collection were in the fields of cardiovascular, respiratory and renal physiology. Results: The MOST developed evidence-based patient models that mimic the physiology believed to be induced by human exposure to a microgravity environment. These models have been integrated into space-relevant scenarios using a human patient simulator and ISS medical resources. Discussion: Despite the lack of a set of physiological parameters representing 'space normal,' the MOST developed space-relevant patient models that mimic microgravity-induced changes in terrestrial physiology. These models are used in clinical scenarios that will medically train flight surgeons, biomedical flight controllers (biomedical engineers; BME) and, eventually, astronaut-crew medical officers (CMO).
A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.
Advances in simulation technology are discussed by a number of government and industry experts, for both training and research and development applications. Advanced techniques, such as helmet-mounted information displays, neurocontrollers, automated training systems, and simulation for space-based systems are included. Advances in training methodology for air transportation are covered by a group of experts in that field, including discussions of advanced flight deck transition training, new training tools, and effective low cost alternatives for part-task training. With the ever-increasing emphasis on human factors in cockpit and cabin design, the section on research, advances, and certification criteria in that field is pertinent. NASA, aircraft manufacturing, and FAA representatives have compiled an informative group of presentations concerning active topics and considerations in human factors design.
Prior to the flight evaluation, the two-segment profile capabilities of the DC-8-61 were evaluated and flight procedures were developed in a flight simulator at the UA Flight Training Center in Denver, Colorado. The flight evaluation reported was conducted to determine the validity of the simulation results, further develop the procedures and use of the area navigation system in the terminal area, certify the system for line operation, and obtain evaluations of the system and procedures by a number of pilots from the industry. The full area navigation capabilities of the special equipment installed were developed to provide terminal area guidance for two-segment approaches. The objectives of this evaluation were: (1) perform an engineering flight evaluation sufficient to certify the two-segment system for the six-month in-service evaluation; (2) evaluate the suitability of a modified RNAV system for flying two-segment approaches; and (3) provide evaluation of the two-segment approach by management and line pilots.
The above real-time training (ARTT) concept is an approach to teaching high-performance skills. ARTT refers to a training paradigm that places the operator in a simulated environment that functions at faster than normal time. It represents a departure from the intuitive, but not often supported, feeling that the best practice is determined by the training environment with the highest fidelity. This approach is hypothesized to provide greater 'transfer value' per simulation trial, by incorporating training techniques and instructional features into the simulator. Two related experiments are discussed. In the first, 25 naive male subjects performed three tank gunnery tasks on a simulator under varying levels of time acceleration (i.e., 1.0x, 1.6x, 2.0x, sequential, and mixed). They were then transferred to a standard (1.0x) condition for testing. Every accelerated condition or combination of conditions produced better training and transfer than the standard condition. Most effective was the presentation of trials at 1.0x, 1.6x, and 2.0x in a random order during training. Overall, the best ARTT group scored about 50 percent higher and trained in 25 percent less time compared to the real-time control group. In the second experiment, 24 mission-capable F-16 pilots performed three tasks on a part-task F-16A flight simulator under varying levels of time compression (i.e., 1.0x, 1.5x, 2.0x, and random). All subjects were then tested in a real-time environment. The emergency procedure (EP) task results showed increased accuracy for the ARTT groups. In testing (transfer), the ARTT groups not only performed the EP more accurately, but dealt with a simultaneous enemy significantly better than a real-time control group. Although the findings on an air combat maneuvering task and stern conversion task were mixed, most measures indicated that the ARTT groups performed better and faster than a real-time control group. Other implications for ARTT are discussed along with future research directions.
This paper intends to help establish fidelity criteria to accompany the simulator motion system diagnostic test specified by the International Civil Aviation Organization. Twelve air- line transport pilots flew three tasks in the NASA Vertical Motion Simulator under four different motion conditions. The experiment used three different hexapod motion configurations, each with a different tradeoff between motion filter gain and break frequency, and one large motion configuration that utilized as much of the simulator's motion space as possible. The motion condition significantly affected: 1) pilot motion fidelity ratings, and sink rate and lateral deviation at touchdown for the approach and landing task, 2) pilot motion fidelity ratings, roll deviations, maximum pitch rate, and number of stick shaker activations in the stall task, and 3) heading deviation after an engine failure in the takeoff task. Significant differences in pilot-vehicle performance were used to define initial objective motion cueing criteria boundaries. These initial fidelity boundaries show promise but need refinement.
A matched pair of computer algorithms determines whether line of sight (LOS) is obstructed by terrain. These algorithms were originally designed for use in conjunction with combat-simulation software in military training exercises, but could also be used for such commercial purposes as evaluating lines of sight for antennas or determining what can be seen from a "room with a view." The quadtree preparation algorithm operates on an array of digital elevation data and only needs to be run once for a terrain region, which can be quite large. Relatively little computation time is needed, as each elevation value is considered only one and one-third times. The LOS assessment algorithm uses that quadtree to answer LOS queries. To determine whether LOS is obstructed, a piecewise-planar (or higher-order) terrain skin is computationally draped over the digital elevation data. Adjustments are made to compensate for curvature of the Earth and for refraction of the LOS by the atmosphere. Average computing time appears to be proportional to the number of queries times the logarithm of the number of elevation data points. Accuracy is as high as is possible for the available elevation data, and symmetric results are assured. In the simulation, the LOS query program runs as a separate process, thereby making more random-access memory available for other computations.
A computer simulation was performed to test the conjecture that, when the sizes of the training sets are small, classification in a subspace of the original data space may give rise to a smaller probability of error than the classification in the data space itself; this is because the gain in the accuracy of estimation of the likelihood functions used in classification in the lower dimensional space (subspace) offsets the loss of information associated with dimensionality reduction (feature extraction). A number of pseudo-random training and data vectors were generated from two four-dimensional Gaussian classes. A special algorithm was used to create an optimal one-dimensional feature space on which to project the data. When the sizes of the training sets are small, classification of the data in the optimal one-dimensional space is found to yield lower error rates than the one in the original four-dimensional space.
Nitric oxide (NO) planar induced laser fluorescence (PLIF) measurements have been performed in a small scale scramjet combustor at the University of Virginia Aerospace Research Laboratory at nominal simulated Mach 5 flight. A mixture of NO and N2 was injected at the upstream end of the inlet isolator as a surrogate for ethylene fuel, and the mixing of this fuel simulant was studied with and without a shock train. The shock train was produced by an air throttle, which simulated the blockage effects of combustion downstream of the cavity flame holder. NO PLIF signal was imaged in a plane orthogonal to the freestream at the leading edge of the cavity. Instantaneous planar images were recorded and analyzed to identify the most uniform cases, which were achieved by varying the location of the fuel injection and shock train. This method was used to screen different possible fueling configurations to provide optimized test conditions for follow-on combustion measurements using ethylene fuel. A theoretical study of the selected NO rotational transitions was performed to obtain a LIF signal that is linear with NO mole fraction and approximately independent of pressure and temperature.
A Persistent Chemical Agent Simulation System was developed (PCASS) to simulate, for force-on-force training exercises, the field environment produced by the presence of persistent chemical agents. Such a simulant system must satisfy several requirements to be of value as a training aid. Specifically, it must provide for realistic training which will generate competency in at least the following areas: (1) detection of the persistent agent presence; (2) proper use of protective equipment and procedures; (3) determination of the extent of contamination; and (4) decontamination of equipment and personnel.
Simulation of turbulence as it relates to the flight training environment is discussed in general terms. Simulators that put random white noise into the system and simulators that put random motion into the equations of motion are discussed, as are simulators that incorporate pitch and roll moments into vertical turbulence. Wind shear models and simplified models of microburst phenomena are covered.