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NASA NTRS2016

Status of Computational Aerodynamic Modeling Tools for Aircraft Loss-of-Control

A concerted effort has been underway over the past several years to evolve computational capabilities for modeling aircraft loss-of-control under the NASA Aviation Safety Program. A principal goal has been to develop reliable computational tools for predicting and analyzing the non-linear stability & control characteristics of aircraft near stall boundaries affecting safe flight, and for utilizing those predictions for creating augmented flight simulation models that improve pilot training. Pursuing such an ambitious task with limited resources required the forging of close collaborative relationships with a diverse body of computational aerodynamicists and flight simulation experts to leverage their respective research efforts into the creation of NASA tools to meet this goal. Considerable progress has been made and work remains to be done. This paper summarizes the status of the NASA effort to establish computational capabilities for modeling aircraft loss-of-control and offers recommendations for future work.

Frink, Neal T.↗
NASA NTRS2014

Upgrading Custom Simulink Library Components for Use in Newer Versions of Matlab

The Spaceport Command and Control System (SCCS) at Kennedy Space Center (KSC) is a control system for monitoring and launching manned launch vehicles. Simulations of ground support equipment (GSE) and the launch vehicle systems are required throughout the life cycle of SCCS to test software, hardware, and procedures to train the launch team. The simulations of the GSE at the launch site in conjunction with off-line processing locations are developed using Simulink, a piece of Commercial Off-The-Shelf (COTS) software. The simulations that are built are then converted into code and ran in a simulation engine called Trick, a Government off-the-shelf (GOTS) piece of software developed by NASA. In the world of hardware and software, it is not uncommon to see the products that are utilized be upgraded and patched or eventually fade away into an obsolete status. In the case of SCCS simulation software, Matlab, a MathWorks product, has released a number of stable versions of Simulink since the deployment of the software on the Development Work Stations in the Linux environment (DWLs). The upgraded versions of Simulink has introduced a number of new tools and resources that, if utilized fully and correctly, will save time and resources during the overall development of the GSE simulation and its correlating documentation. Unfortunately, simply importing the already built simulations into the new Matlab environment will not suffice as it will produce results that may not be expected as they were in the version that is currently being utilized. Thus, an upgrade execution plan was developed and executed to fully upgrade the simulation environment to one of the latest versions of Matlab.

Matlab Simulation↗
NASA NTRS1984

Determining Training Device Requirements in Army Aviation Systems

A decision making methodology which applies the systems approach to the training problem is discussed. Training is viewed as a total system instead of a collection of individual devices and unrelated techniques. The core of the methodology is the use of optimization techniques such as the transportation algorithm and multiobjective goal programming with training task and training device specific data. The role of computers, especially automated data bases and computer simulation models, in the development of training programs is also discussed. The approach can provide significant training enhancement and cost savings over the more traditional, intuitive form of training development and device requirements process. While given from an aviation perspective, the methodology is equally applicable to other training development efforts.

Poumade, M. L.↗
NASA NTRS2005

Mission leverage education: NSU/NASA innovative undergraduate model

The BEST Lab (Center for Excellence in Science Education), the Center for Materials Research (CMR), and the Chemistry, Mathematics, Physics, and Computer Science (CS) Departments at Norfolk State University (NSU) joined forces to implement MiLEN(2) IUM - an innovative approach tu integrate current and emerging research into the undergraduate curricula and train students on NASA-related fields. An Earth Observing System (EOS) mission was simulated where students are educated and trained in many aspects of Remote Sensing: detector physics and spectroscopy; signal processing; data conditioning, analysis, visualization; and atmospheric science. This model and its continued impact is expected to significantly enhance the quality of the Mathematics, Science, Engineering and Technology (MSET or SMET) educational experience and to inspire students from historically underrepresented groups to pursue careers in NASA-related fields. MiLEN(2) IUM will be applicable to other higher education institutions that are willing to make the commitment to this endeavor in terms of faculty interest and space.

Chaudhury, S. Raj↗
NASA NTRS2003

NASA Virtual Glovebox: An Immersive Virtual Desktop Environment for Training Astronauts in Life Science Experiments

The International Space Station will soon provide an unparalleled research facility for studying the near- and longer-term effects of microgravity on living systems. Using the Space Station Glovebox Facility - a compact, fully contained reach-in environment - astronauts will conduct technically challenging life sciences experiments. Virtual environment technologies are being developed at NASA Ames Research Center to help realize the scientific potential of this unique resource by facilitating the experimental hardware and protocol designs and by assisting the astronauts in training. The Virtual GloveboX (VGX) integrates high-fidelity graphics, force-feedback devices and real- time computer simulation engines to achieve an immersive training environment. Here, we describe the prototype VGX system, the distributed processing architecture used in the simulation environment, and modifications to the visualization pipeline required to accommodate the display configuration.

Twombly, I. Alexander↗
NASA NTRS2001

Effects of Above Real Time Training (ARTT) On Individual Skills and Contributions to Crew/Team Performance

Above Real Time Training (ARTT) is the training acquired on a real time simulator when it is modified to present events at a faster pace than normal. The experiments on training of pilots performed by NASA engineers and others have indicated that real time training (RTT) reinforced with ARTT would offer an effective training strategy for such tasks which require significant effort at time and workload management. A study was conducted to find how ARTT and RTT complement each other for training of novice pilot-navigator teams to fly on a required route. In the experiment, each of the participating pilot-navigator teams was required to conduct simulator flights on a prescribed two-legged ground track while maintaining required air speed and altitude. At any instant in a flight, the distance between the actual spatial point location of the airplane and the required spatial point was used as a measure of deviation from the required route. A smaller deviation represented better performance. Over a segment of flight or over complete flight, an average value of the deviation represented consolidated performance. The deviations were computed from the information on latitude, longitude, and altitude. In the combined ARTT and RTT program, ARTT at intermediate training intervals was beneficial in improving the real time performance of the trainees. It was observed that the team interaction between pilot and navigator resulted in maintaining high motivation and active participation throughout the training program.

Ali, Syed Firasat↗
NASA NTRS1959

Studies of Pilot Control During Launching and Reentry of Space Vehicles, Utilizing the Human Centrifuge

With the ever increasing complexity of airplanes and the nearness to reality of manned space vehicles the use of pilot-controlled flight simulators has become imperative. The state of the art in flight simulation has progressed well with the demand. Pilot-controlled flight simulators are finding increasing uses in aeromedical research, airplane and airplane systems design, and preflight training. At the present many flight simulators are in existence with various degrees of sophistication and sundry purposes. These vary from fixed base simulators where the pilot applies control inputs according to visual cues presented to him on an instrument display to moving base simulators where various combinations of angular and linear motions are added in an attempt to improve the flight simulation.

Clark, Carl C.↗
NASA NTRS1986

Technical evaluation report on the Flight Mechanics Panel Symposium on Flight Simulation

In recent years, important advances were made in technology both for ground-based and in-flight simulators. There was equally a broadening of the use of flight simulators for research, development, and training purposes. An up-to-date description of the state-of-the-art of technology and engineering was provided for both ground-based and in-flight simulators and their respective roles were placed in context within the aerospace scene.

Cook, Anthony M.↗
NASA NTRS2010

The Right Stuff: A Look Back at Three Decades of Flight Controller Training for Space Shuttle Mission Operations

This paper will summarize the thirty-year history of Space Shuttle operations from the perspective of training in NASA Johnson Space Center's Mission Control Center. It will focus on training and development of flight controllers and instructors, and how training practices have evolved over the years as flight experience was gained, new technologies developed, and programmatic needs changed. Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. This paper will give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams are trained and certified. The training methodology for developing flight controllers has evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. Flight controllers will share their experiences in training and operating the Space Shuttle throughout the Program s history. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The audience will learn what it is like to perform a simulation as a shuttle flight controller. Finally, we will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors.

Dittemore, Gary D.↗
NASA NTRSDate not supplied

Initial Testing the Exploration Extravehicular Mobility Unit (xEMU) in Lunar Environment Simulation at the Neutral Buoyancy Lab (NBL) in 2022

Understanding how to effectively train for Extravehicular Activities (EVAs) for Artemis missions is critical. Tasked by the new Extravehicular Activity and Human Surface Mobility Program(EHP), a team of stakeholders from engineering and flight operations worked together to test the Exploration Extravehicular Mobility (xEMU) in the Neutral Buoyancy Lab(NBL) simulating the lunar environment. The NBL is a pool in which astronauts are typically trained to complete simulated International Space Station (ISS) EVA tasks in a neutral weigh-out. The overall focus for this test series was evaluating how well the NBL could be used as an effective simulated environment for Artemis lunar EVAs. New NBL support hardware was manufactured to support this test series and update the interfaces to be more representative of xEMU hardware such as a new NBL Portable Life Support System(PLSS)mock-up and umbilical, lights and cameras, and display and control unit (DCU).To simulate the 1/6th-gravity (1/6-g) environment, extra weights were added around the suit to create an accurate center of gravity representation. The Partial Gravity Weigh-out Stand (PGWS) predicted where and how much weight would be required for the 1/6th-G simulation and predicted the center of gravity placement. After the weigh-out attempt was completed, the suited participant would complete a set of directed tasks such as kneeling, object pick-up, and walking to evaluate the weigh-out. To create the lunar environment, sand, boulders, and ramps were added to the pool floor. If time allowed after weigh-out was determined acceptable, the flight operations team would direct the suited participants to complete simulated Artemis EVA tasks using flight-like tools and tool cart. The team also evaluated turning out the overhead lights in the NBL for a portion of some of the test runs and using a solar simulator to create amore realistic lunar south pole lighting environment. The test series completed one unmanned test and nine manned tests. With four of the tests, dual xEMU suited operations were completed with subjects in both the small and large xEMU sizes. Overall, the NBL was able to create an acceptable lunar landscape and ability to have two subjects work side by side in pressurized xEMUs which currently cannot be achieved in other test environments. Achieving a stable 1/6th-G lunar weigh-out and clear, consistent dual suit communications was a challenge for this series. Future work is planned to continue to improve this simulation environment.

spacesuit↗
NASA NTRSDate not supplied

Initial Testing of the Exploration Extravehicular Mobility Unit (xEMU) in Lunar Environment Simulation at the Neutral Buoyancy Lab (NBL) in 2022

Understanding how to effectively train for Extravehicular Activities (EVAs) for Artemis missions is critical. Tasked by the new Extravehicular Activity and Human Surface Mobility Program(EHP), a team of stakeholders from engineering and flight operations worked together to test the Exploration Extravehicular Mobility (xEMU) in the Neutral Buoyancy Lab(NBL) simulating the lunar environment. The NBL is a pool in which astronauts are typically trained to complete simulated International Space Station (ISS) EVA tasks in a neutral weigh-out. The overall focus for this test series was evaluating how well the NBL could be used as an effective simulated environment for Artemis lunar EVAs. New NBL support hardware was manufactured to support this test series and update the interfaces to be more representative of xEMU hardware such as a new NBL Portable Life Support System(PLSS)mock-up and umbilical, lights and cameras, and display and control unit (DCU).To simulate the 1/6th-gravity (1/6-g) environment, extra weights were added around the suit to create an accurate center of gravity representation. The Partial Gravity Weigh-out Stand (PGWS) predicted where and how much weight would be required for the 1/6th-G simulation and predicted the center of gravity placement. After the weigh-out attempt was completed, the suited participant would complete a set of directed tasks such as kneeling, object pick-up, and walking to evaluate the weigh-out. To create the lunar environment, sand, boulders, and ramps were added to the pool floor. If time allowed after weigh-out was determined acceptable, the flight operations team would direct the suited participants to complete simulated Artemis EVA tasks using flight-like tools and tool cart. The team also evaluated turning out the overhead lights in the NBL for a portion of some of the test runs and using a solar simulator to create amore realistic lunar south pole lighting environment. The test series completed one unmanned test and nine manned tests. With four of the tests, dual xEMU suited operations were completed with subjects in both the small and large xEMU sizes. Overall, the NBL was able to create an acceptable lunar landscape and ability to have two subjects work side by side in pressurized xEMUs which currently cannot be achieved in other test environments. Achieving a stable 1/6th-G lunar weigh-out and clear, consistent dual suit communications was a challenge for this series. Future work is planned to continue to improve this simulation environment.

spacesuit↗
NASA NTRS2011

The Right Stuff: A Look Back at Three Decades of Flight Controller Training for Space Shuttle Mission Operations

Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. This paper will give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams are trained and certified. The training methodology for developing flight controllers has evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. Flight controllers will share their experiences in training and operating the Space Shuttle throughout the Program s history. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The reader will learn what it is like to perform a simulation as a shuttle flight controller. Finally, the paper will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors. These endeavors could range from going to the moon or to Mars. The lessons learned from operating the space shuttle for over thirty years will help the space industry build the next human transport space vehicle and inspire the next generation of space explorers.

Dittemore, Gary D.↗
NASA NTRS2011

The Final Count Down: A Review of Three Decades of Flight Controller Training Methods for Space Shuttle Mission Operations

Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. As the space shuttle program ends in 2011, a review of how training for STS-1 was conducted compared to STS-134 will show multiple changes in training of shuttle flight controller over a thirty year period. This paper will additionally give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams have been trained certified over the life span of the space shuttle. The training methods for developing flight controllers have evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The reader will learn what it is like to perform a simulation as a shuttle flight controller. Finally, the paper will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors.

Dittemore, Gary D.↗
NASA NTRS2002

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Rajkumar, T.↗
NASA NTRS1984

The Shuttle Mission Simulator computer generated imagery

Equipment available in the primary training facility for the Space Transportation System (STS) flight crews includes the Fixed Base Simulator, the Motion Base Simulator, the Spacelab Simulator, and the Guidance and Navigation Simulator. The Shuttle Mission Simulator (SMS) consists of the Fixed Base Simulator and the Motion Base Simulator. The SMS utilizes four visual Computer Generated Image (CGI) systems. The Motion Base Simulator has a forward crew station with six-degrees of freedom motion simulation. Operation of the Spacelab Simulator is planned for the spring of 1983. The Guidance and Navigation Simulator went into operation in 1982. Aspects of orbital visual simulation are discussed, taking into account the earth scene, payload simulation, the generation and display of 1079 stars, the simulation of sun glare, and Reaction Control System jet firing plumes. Attention is also given to landing site visual simulation, and night launch and landing simulation.

Henderson, T. H.↗
NASA NTRS1984

Low cost training aids and devices

The need for advanced flight simulators for two engine aircraft is discussed. Cost effectiveness is a major requirement. Other training aids available for increased effectiveness are recommended. Training aids include: (1) audio-visual slides; (2) information transfer; (3) programmed instruction; and (4) interactive training systems.

Lawver, J.↗
NASA NTRS1993

A Search of Large Amplitude Waves in Slow Shock in the Distant Tail and an Examination of Wave Turbulence in the Plasma Sheet Boundary Layer

Two fluid simulations predict that switch-off slow shocks should have large amplitude left-hand wave trains downstream of the shock. Recent simulations show that this feature depends on specific plasma parameters, such as Much number and ratio of electron-to-ion temperature. In this study we carefully examine all distant tail crossings using ISEE-3 field and plasma data. We seek large amplitude rotational waves and compare to shock parameters. We also investigate right-hand waves associated with ion beams in the plasma sheet boundary layer. We examine the role of those back-streaming ions from slow shocks in wave generation. Wave mode cascade and parametric decay in these regions also will be studied.

Ho, C. M.↗
NASA NTRS2012

Simulation Modeling Requirements for Loss-of-Control Accident Prevention of Turboprop Transport Aircraft

In-flight loss of control remains the leading contributor to aviation accident fatalities, with stall upsets being the leading causal factor. The February 12, 2009. Colgan Air, Inc., Continental Express flight 3407 accident outside Buffalo, New York, brought this issue to the forefront of public consciousness and resulted in recommendations from the National Transportation Safety Board to conduct training that incorporates stalls that are fully developed and develop simulator standards to support such training. In 2010, Congress responded to this accident with Public Law 11-216 (Section 208), which mandates full stall training for Part 121 flight operations. Efforts are currently in progress to develop recommendations on implementation of stall training for airline pilots. The International Committee on Aviation Training in Extended Envelopes (ICATEE) is currently defining simulator fidelity standards that will be necessary for effective stall training. These recommendations will apply to all civil transport aircraft including straight-wing turboprop aircraft. Government-funded research over the previous decade provides a strong foundation for stall/post-stall simulation for swept-wing, conventional tail jets to respond to this mandate, but turboprops present additional and unique modeling challenges. First among these challenges is the effect of power, which can provide enhanced flow attachment behind the propellers. Furthermore, turboprops tend to operate for longer periods in an environment more susceptible to ice. As a result, there have been a significant number of turboprop accidents as a result of the early (lower angle of attack) stalls in icing. The vulnerability of turboprop configurations to icing has led to studies on ice accumulation and the resulting effects on flight behavior. Piloted simulations of these effects have highlighted the important training needs for recognition and mitigation of icing effects, including the reduction of stall margins. This paper addresses simulation modeling requirements that are unique to turboprop transport aircraft and highlights the growing need for aerodynamic models suitable for stall training for these configurations. A review of prominent accidents that involved aerodynamic stall is used to illustrate various modeling features unique to turboprop configurations and the impact of stall behavior on susceptibility to loss of control that has led to new training requirements. This is followed by an overview of stability and control behavior of straight-wing turboprops, the related aerodynamic characteristics, and a summary of recent experimental studies on icing effects. In addition, differences in flight dynamics behavior between swept-wing jets and straight-wing turboprop configurations are discussed to compare and contrast modeling requirements. Specific recommendations for aerodynamic models along with further research needs and data measurements are also provided. 1

Crider, Dennis↗