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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Measurement and Validation of Bidirectional Reflectance of Space Shuttle and Space Station Materials for Computerized Lighting Models

Task illumination has a major impact on human performance: What a person can perceive in his environment significantly affects his ability to perform tasks, especially in space's harsh environment. Training for lighting conditions in space has long depended on physical models and simulations to emulate the effect of lighting, but such tests are expensive and time-consuming. To evaluate lighting conditions not easily simulated on Earth, personnel at NASA Johnson Space Center's (JSC) Graphics Research and Analysis Facility (GRAF) have been developing computerized simulations of various illumination conditions using the ray-tracing program, Radiance, developed by Greg Ward at Lawrence Berkeley Laboratory. Because these computer simulations are only as accurate as the data used, accurate information about the reflectance properties of materials and light distributions is needed. JSC's Lighting Environment Test Facility (LETF) personnel gathered material reflectance properties for a large number of paints, metals, and cloths used in the Space Shuttle and Space Station programs, and processed these data into reflectance parameters needed for the computer simulations. They also gathered lamp distribution data for most of the light sources used, and validated the ability to accurately simulate lighting levels by comparing predictions with measurements for several ground-based tests. The result of this study is a database of material reflectance properties for a wide variety of materials, and lighting information for most of the standard light sources used in the Shuttle/Station programs. The combination of the Radiance program and GRAF's graphics capability form a validated computerized lighting simulation capability for NASA.

Fletcher, Lauren E.↗
NASA NTRS1998

X-33 Integrated Test facility, Extended Range Simulation

In support of the X-33 Single Stage To Orbit program, NASA Dryden Flight Research Center was selected to provide continuous communications coverage of the X-33 vehicle from launch, through landing at Malmstrom Air Force Base, Montana and Michaels Army Air Field, Utah. An extensive real-time range simulation capability is being developed to ensure successful communications with the autonomous X-33 vehicle. This paper will provide an overview of the various levels of simulation being developed to support the X-33 extended range subsystems. These subsystems include the Flight Termination System, L-Band command uplink subsystem and the S-Band telemetry downlink subsystem. In addition, the radar model developed provides continuous azimuth, elevation and range information based on the flight trajectory. The Dynamic Ground Station Analysis model developed by NASA Goddard Space Flight Center, calculate the received signal strength at each ground station. This model takes into consideration Radio Frequency (RF) link parameters such as frequency, antenna gain, space loss, plasma effects and the vehicle's position and attitude at any point in time during the flight path. All three RF links are then attenuated based on this calculated level and the RF signals are sent into telemetry receivers to emulate remote sites, or the power incident on the vehicle from uplinked signals. The best source received telemetry data is then passed back to the Launch and Mission Control Monitoring System (LMCMS) resident in the Operations Control Center. The LMCMS also provides the range simulation system the uplink command combined with differential GPS corrections. Later stages will require the progressive integration of actual range hardware with this simulation effort, leading to communication between telemetry, uplink and FTS antennas at NASA Dryden Flight Research Center, with vehicle antennas mounted on the Walter C. Williams Research Aircraft Integration Facility (RAIF). Decommutated Pulse Code Modulated (PCM) data is displayed on one of the four monitors that comprise the Range Safety Officer's (RSO) station. Also displayed are instantaneous impact prediction models, and Federal Aviation Administration (FAA) data for notification of other traffic in the area. Aside from initiating the flight termination command and validating communication links, the RSO station with the range simulation will be used to provide both range control and range safety officers training. The training is necessary to perform their respective functions with greater levels of confidence prior to first flight.

Sharma, Ashley↗
NASA NTRS1994

Apollo 11 Facts Project [Prelaunch Press Conference/EVA Training]

A prelaunch press conference shows the crewmembers of Apollo 11, Commander Neil A. Armstrong, Lunar Module Pilot Edwin E. Aldrin, Jr., and Command Module Pilot Michael Collins, answering questions about their upcoming mission (this section has sound, the rest of the video is without sound). Footage is seen of the crew during training for the extravehicular activity portion of the mission and using the flight simulator.

Source record↗
NASA NTRS2006

Comparison of Bystander Cardiopulmonary Resuscitation (BCPR) Performance in the Absence and Presence of Timing Devices for Coordinating Delivery of Ventilatory Breaths and Cardiac Compressions in a Model of Adult Cardiopulmonary Arrest

Astronaut crew medical officers (CMO) aboard the International Space Station (ISS) receive 40 hours of medical training during the 18 months preceding each mission. Part of this training ilncludes twoperson cardiopulmonary resuscitation (CPR) per training guidelines from the American Heart Association (AHA). Recent studies concluded that the use of metronomic tones improves the coordination of CPR by trained clinicians. Similar data for bystander or "trained lay people" (e.g. CMO) performance of CPR (BCPR) have been limited. The purpose of this study was to evailuate whether use of timing devices, such as audible metronomic tones, would improve BCPR perfomance by trained bystanders. Twenty pairs of bystanders trained in two-person BCPR performled BCPR for 4 minutes on a simulated cardiopulmonary arrest patient using three interventions: 1) BCPR with no timing devices, 2) BCPR plus metronomic tones for coordinating compression rate only, 3) BCPR with a timing device and metronome for coordinating ventilation and compression rates, respectively. Bystanders were evaluated on their ability to meet international and AHA CPR guidelines. Bystanders failed to provide the recommended number of breaths and number of compressions in the absence of a timing device and in the presence of audible metronomic tones for only coordinating compression rate. Bystanders using timing devices to coordinate both components of BCPR provided the reco number of breaths and were closer to providing the recommended number of compressions compared with the other interventions. Survey results indicated that bystanders preferred to use a metronome for delivery of compressions during BCPR. BCPR performance is improved by timing devices that coordinate both compressions and breaths.

Hurst, Victor, IV↗
NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗
NASA NTRSDate not supplied

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVAs on the Moon and Mars are expected to be higher compared to microgravity EVAs currently conducted from the International Space Station. Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We conducted a cognitive task analysis with 15 astronauts and subject matter experts in EVA operations and research. Interviews focused on surface exploration EVA and elicited feedback from experts on the cognitive skills required for specific EVA tasks, including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis informs ongoing exploration EVA for Moon to Mars. This work identifies the specific cognitive challenges that astronauts are likely to encounter during surface exploration EVA, and provides the foundation for: (1) prioritized and targeted cognitive performance measurement and functional performance tests, (2) EVA simulation design at varying levels of cognitive workload, and (3) the development of training and other technologies that can improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
NASA NTRS1998

Neural Networks for Rapid Design and Analysis

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Sparks, Dean W., Jr.↗
NASA NTRS2016

Flight Operations

Outline the process that takes new hires with zero knowledge all the way to the point of completing missions in Flight Operations. Audience members should be able to outline the attributes of a flight controller and instructor, outline the training flow for flight controllers and instructors, and identify how the flight controller and instructor attributes are necessary to ensure operational excellence in mission prep and execution. Identify how the simulation environment is used to develop crisis management, communication, teamwork, and leadership skills for SGT employees beyond what can be provided by classroom training.

Forest, Greg↗
NASA NTRS1994

A Piloted Simulation Study of Advanced Controls and Displays for General Aviation Airplanes

A decoupled, fly-by-wire control system combined with a pictorial head-up display has been developed and evaluated on a piloted simulator. The test subjects used in the evaluation were primarily non-pilots who were given no training or practice before the data runs. Despite their lack of experience and training, all of the test subjects were able to complete a complex piloting task on their very first run. The piloting task was performed in reduced visibility conditions and consisted of a continuous series of common maneuvers beginning with a takeoff and ending with a landing. Severe upsets from turbulence and emergency situations such as engine failure were not simulated. Quantitative qualitative data are presented to illustrate the large improvements in subject performance when using the advanced controls and displays compared to the conventional ones.

Eric C. Stewart↗
NASA NTRS2000

Infrastructure for Training and Partnershipes: California Water and Coastal Ocean Resources

The purpose of this project was to advance the existing ICESS/Bren School computing infrastructure to allow scientists, students, and research trainees the opportunity to interact with environmental data and simulations in near-real time. Improvements made with the funding from this project have helped to strengthen the research efforts within both units, fostered graduate research training, and helped fortify partnerships with government and industry. With this funding, we were able to expand our computational environment in which computer resources, software, and data sets are shared by ICESS/Bren School faculty researchers in all areas of Earth system science. All of the graduate and undergraduate students associated with the Donald Bren School of Environmental Science and Management and the Institute for Computational Earth System Science have benefited from the infrastructure upgrades accomplished by this project. Additionally, the upgrades fostered a significant number of research projects (attached is a list of the projects that benefited from the upgrades). As originally proposed, funding for this project provided the following infrastructure upgrades: 1) a modem file management system capable of interoperating UNIX and NT file systems that can scale to 6.7 TB, 2) a Qualstar 40-slot tape library with two AIT tape drives and Legato Networker backup/archive software, 3) previously unavailable import/export capability for data sets on Zip, Jaz, DAT, 8mm, CD, and DLT media in addition to a 622Mb/s Internet 2 connection, 4) network switches capable of 100 Mbps to 128 desktop workstations, 5) Portable Batch System (PBS) computational task scheduler, and vi) two Compaq/Digital Alpha XP1000 compute servers each with 1.5 GB of RAM along with an SGI Origin 2000 (purchased partially using funds from this project along with funding from various other sources) to be used for very large computations, as required for simulation of mesoscale meteorology or climate.

Siegel, David A.↗
NASA NTRS1991

An Intelligent Computer-aided Training System (CAT) for Diagnosing Adult Illiterates: Integrating NASA Technology into Workplace Literacy

An important part of NASA's mission involves the secondary application of its technologies in the public and private sectors. One current application being developed is The Adult Literacy Evaluator, a simulation-based diagnostic tool designed to assess the operant literacy abilities of adults having difficulties in learning to read and write. Using Intelligent Computer-Aided Training (ICAT) system technology in addition to speech recognition, closed-captioned television (CCTV), live video and other state-of-the-art graphics and storage capabilities, this project attempts to overcome the negative effects of adult literacy assessment by allowing the client to interact with an intelligent computer system which simulates real-life literacy activities and materials and which measures literacy performance in the actual context of its use. The specific objectives of the project are as follows: (1) to develop a simulation-based diagnostic tool to assess adults' prior knowledge about reading and writing processes in actual contexts of application; (2) to provide a profile of readers' strengths and weaknesses; and (3) to suggest instructional strategies and materials which can be used as a beginning point for remediation. In the first and development phase of the project, descriptions of literacy events and environments are being written and functional literacy documents analyzed for their components. From these descriptions, scripts are being generated which define the interaction between the student, an on-screen guide and the simulated literacy environment.

Yaden, David B., Jr.↗
NASA NTRS2016

NASA Operational Simulator for Small Satellites: Tools for Software Based Validation and Verification of Small Satellites

The NASA Operational Simulator for Small Satellites (NOS3) is a suite of tools to aid in areas such as software development, integration test (IT), mission operations training, verification and validation (VV), and software systems check-out. NOS3 provides a software development environment, a multi-target build system, an operator interface-ground station, dynamics and environment simulations, and software-based hardware models. NOS3 enables the development of flight software (FSW) early in the project life cycle, when access to hardware is typically not available. For small satellites there are extensive lead times on many of the commercial-off-the-shelf (COTS) components as well as limited funding for engineering test units (ETU). Considering the difficulty of providing a hardware test-bed to each developer tester, hardware models are modeled based upon characteristic data or manufacturers data sheets for each individual component. The fidelity of each hardware models is such that FSW executes unaware that physical hardware is not present. This allows binaries to be compiled for both the simulation environment, and the flight computer, without changing the FSW source code. For hardware models that provide data dependent on the environment, such as a GPS receiver or magnetometer, an open-source tool from NASA GSFC (42 Spacecraft Simulation) is used to provide the necessary data. The underlying infrastructure used to transfer messages between FSW and the hardware models can also be used to monitor, intercept, and inject messages, which has proven to be beneficial for VV of larger missions such as James Webb Space Telescope (JWST). As hardware is procured, drivers can be added to the environment to enable hardware-in-the-loop (HWIL) testing. When strict time synchronization is not vital, any number of combinations of hardware components and software-based models can be tested. The open-source operator interface used in NOS3 is COSMOS from Ball Aerospace. For testing, plug-ins are implemented in COSMOS to control the NOS3 simulations, while the command and telemetry tools available in COSMOS are used to communicate with FSW. NOS3 is actively being used for FSW development and component testing of the Simulation-to-Flight 1 (STF-1) CubeSat. As NOS3 matures, hardware models have been added for common CubeSat components such as Novatel GPS receivers, ClydeSpace electrical power systems and batteries, ISISpace antenna systems, etc. In the future, NASA IVV plans to distribute NOS3 to other CubeSat developers and release the suite to the open-source community.

Verification↗
NASA NTRS2016

System and Method for Outlier Detection via Estimating Clusters

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Iverson, David J.↗
NASA NTRS1999

1999 Marsokhod Field Experiment: A Simulation of a Mars Rover Science Mission

A field experiment to simulate a rover mission to Mars was performed in February 1999. This experiment, the latest in a series of rover field experiments, was designed to demonstrate and validate technologies and investigation strategies for high-science, high-technology performance, and cost-effective planetary rover operations. Objectives: The experiment objectives were to: (1) train scientists in a mission configuration relevant to Surveyor program rover missions at a terrestrial analog field site simulating the criteria of high-priority candidate landing-sites on Mars; (2) develop optimal exploration strategies; (3) evaluate the effectiveness of imaging and spectroscopy in addressing science objectives; (4) assess the value and limitation of descent imaging in supporting rover operations; and (5) evaluate the ability of a science team to correctly interpret the geology of the field site using rover observations. A field site in the California Mojave Desert was chosen for its relevance to the criteria for landing site selection for the Mars Surveyor program. These criteria are: (1) evidence of past water activity; (2) presence of a mechanism to concentrate life; (3) presence of thermal energy sources; (4) evidence of rapid burial; and (5) excavation mechanisms that could expose traces of life.

Stoker, C.↗
NASA NTRS2009

Muscle Volume Increases Following 16 Weeks of Resistive Exercise Training with the Advanced Resistive Exercise Device (ARED) and Free Weights

Space flight-induced muscle atrophy, particularly in the postural and locomotorymuscles, may impair task performance during long-duration space missions and planetary exploration. High intensity free weight (FW) resistive exercise training has been shown to prevent atrophy during bed rest, a space flight analog. NASA developed the Advanced Resistive Exercise Device (ARED) to simulate the characteristics of FW exercise (i.e. constant mass, inertial force) and to be used as a countermeasure during International Space Station (ISS) missions. PURPOSE: To compare the efficacy of ARED and FW training to induce hypertrophy in specific muscle groups in ambulatory subjects prior to deploying ARED on the ISS. METHODS: Twenty untrained subjects were assigned to either the ARED (8 males, 3 females) or FW (6 males, 3 females) group and participated in a periodizedtraining protocol consisting of squat (SQ), heel raise (HR), and deadlift(DL) exercises 3 d wk-1 for 16 wks. SQ, HR, and DL muscle strength (1RM) was measured before, after 8 wks, and after 16 wks of training to prescribe exercise and measure strength changes. Muscle volume of the vastigroup (V), hamstring group (H), hip adductor group (ADD), medial gastrocnemius(MG), lateral gastrocnemius(LG), and deep posterior muscles including soleus(DP) was measured using MRI pre-and post-training. Consecutive cross-sectional images (8 mm slices with a 2 mm gap) were analyzed and summed. Anatomical references insured that the same muscle sections were analyzed pre-and post-training. Two-way repeated measures ANOVAs (p<0.05) were used to test for differences in muscle strength and volume between training devices. RESULTS: SQ, HR, and DL 1RM increased in both FW (SQ: 49+/-6%, HR: 12+/-2%, DL: 23+/-4%) and ARED (SQ: 31+/-4%, HR: 18+/-2%, DL: 23+/-3%) groups. Both groups increased muscle volume in the V (FW: 13+/-2%, ARED: 10+/-2%), H (FW: 3+/-1%, ARED: 3+/-1 %), ADD (FW: 15=/-2%, ARED: 10+/-1%), LG (FW: 7+/-2%, ARED: 4+/-1%), MG (FW: 7+/-2%, ARED: 5+/-2%), and DP (FW: 2+/-1%; ARED: 2+/-1%) after training. There were no between group differences in muscle strength or volume. CONCLUSIONS: The increase in muscle volume and strength following ARED training is not different than FW training. With the training effects similar to FW and a 600 lb load capacity, ARED likely will protect against muscle atrophy in microgravity.

Nash, R. E.↗
NASA NTRS2011

Approximate Simulation of Acute Hypobaric Hypoxia with Normobaric Hypoxia

INTRODUCTION. Some manufacturers of reduced oxygen (O2) breathing devices claim a comparable hypobaric hypoxia (HH) training experience by providing F(sub I) O2 < 0.209 at or near sea level pressure to match the ambient O2 partial pressure (iso-pO2) of the target altitude. METHODS. Literature from investigators and manufacturers indicate that these devices may not properly account for the 47 mmHg of water vapor partial pressure that reduces the inspired partial pressure of O2 (P(sub I) O2). Nor do they account for the complex reality of alveolar gas composition as defined by the Alveolar Gas Equation. In essence, by providing iso-pO2 conditions for normobaric hypoxia (NH) as for HH exposures the devices ignore P(sub A)O2 and P(sub A)CO2 as more direct agents to induce signs and symptoms of hypoxia during acute training exposures. RESULTS. There is not a sufficient integrated physiological understanding of the determinants of P(sub A)O2 and P(sub A)CO2 under acute NH and HH given the same hypoxic pO2 to claim a device that provides isohypoxia. Isohypoxia is defined as the same distribution of hypoxia signs and symptoms under any circumstances of equivalent hypoxic dose, and hypoxic pO2 is an incomplete hypoxic dose. Some devices that claim an equivalent HH experience under NH conditions significantly overestimate the HH condition, especially when simulating altitudes above 10,000 feet (3,048 m). CONCLUSIONS. At best, the claim should be that the devices provide an approximate HH experience since they only duplicate the ambient pO2 at sea level as at altitude (iso-pO2 machines). An approach to reduce the overestimation is to at least provide machines that create the same P(sub I)O2 (iso-P(sub I)O2 machines) conditions at sea level as at the target altitude, a simple software upgrade.

Conkin, J.↗
NASA NTRS1996

Construction of a Piezoresistive Neural Sensor Array

The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors. The construction of these devices may show significant improvement in ability to interrogate signals and in the control of effective compliance. This work focuses on the development a variety of series/parallel interconnected piezoresistive control elements for the neural sensing function.

Carlson, W. B.↗
NASA NTRS2014

Simulation Technology at NASA

NASA Ames Research Center is home to several high-fidelity research flight and air-traffic control simulation facilities which, together with an experienced workforce, produce high-quality research data and findings that have proven to be applicable in the real world. These assets include the Vertical Motion Simulator (VMS), Crew Vehicle Systems Research Facility (CVSRF), Future Flight Central (FFC) air traffic control tower simulator, and several air-traffic control (ATC) simulators. The VMS combines a high-fidelity simulation capability with an adaptable simulation environment, enabling customization for numerous human-in-the-loop research applications. The distinctive feature of the VMS is its unparalleled large amplitude, high-fidelity motion capability. In over 30 years of continuous operation, the VMS has contributed significantly to the body of knowledge in a range of disciplines directly benefiting several aerospace programs and flight safety, including the design and development of flight control systems for the Joint Strike Fighter, Space Shuttle Orbiter, and rotorcraft. It continues to be used for researching new vehicle configurations, vehicle control and safety, transfer-of-training, etc., by NASA, other government agencies, and Industry.The CVSRF includes two motion-based flight simulators: a Boeing 747-400 full flight simulator and the reconfigurable Advanced Concepts Flight Simulator (ACFS). These simulators are primarily used to research air-traffic management concepts and procedures, advanced navigation and avionics concepts, and cockpit human factors. FFC is a full-sized control tower simulator with a 360-degree external field-of-view display system and reconfigurable system architecture. FFC and the ATC simulators are used for testing air-traffic management automation and decision support tools and demonstrate their feasibility in a realistic environment prior to technology transfer for implementation in the National Airspace System (NAS).To support integrated simulations and flight-tests for NASA's Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project, NASA developed a distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts. Development of the software enabling the LVC is conducted primarily at the Distributed Simulation Research Lab (DSRL) at NASA Ames. The LVC components provide the core infrastructure supporting simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This provides the ability to conduct tests more efficiently by promoting the use of existing distributed assets. The LVC infrastructure was used in several human-in-the-loop simulations to evaluate acceptance of Detect and Avoid (DAA) advisories used by UAS pilots to maintain well clear of other virtual traffic and to negotiate maneuvers with air traffic control. It is currently being used to support testing of self-separation algorithms between unmanned and manned aircraft in live flight. Further simulations with more comprehensive air traffic scenarios mixing live and virtual aircraft is planned.In the current fiscal environment, maintaining and upgrading these high-fidelity simulation assets and retaining the skilled workforce necessary to meet future research needs is the primary non-technical challenge.

CVSRF↗