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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

United States Navy - Canadian forces solid state flight data recorder/crash position locator experiment on the B-720 controlled impact demonstration

The operation of a radio beacon position locator during and after the remotely controlled transport aircraft is discussed. The radio beacon transmission was actuated and was picked up by the Navy P-3A chase aircraft for a short time, after which reception was lost. The pilot reported that he received a signal on both 121.5 MHz and 243 MHz for a period of approximately 5 seconds. Five minutes after the crash a portable direction finding unit located on the roof of the NASA Dryden Flight Research Facility, 4 miles distant from the crash, was unable to pick up the beacon transmission. The fire crews started fighting the fires approximately 90 seconds after the time of impact. Navy personnel access to the crash site was allowed on the morning of December 2, 1984. Radio beacon locator was found resting top side up, 15 feet forward and 13 feet perpendicular from the tray location the starboard side of the aircraft. An immediate inspection indicated the airfoil suffered moderate fire damage with paint peeling but not intumescing. The visual marker strobe lamp housings were intact but extensively burned such that it was impossible to see if the lamps had survived. The airfoil suffered minor structural damage, with assorted dents, etc. The extended plunger on the ARU-21 release unit indicated that the pyrotechnic deployment system operated. The radio beacon base (tray) suffered some heat and fire damage, and was charred and blackened by smoke. The frangible switch in the nose survived and the switch in the belly was recovered and found to have actuated. It is assumed that this switch fired the ARU-21 squib. There were no other release switches installed in the normally open system in the aircraft.

Watters, D. M.↗
NASA NTRSDate not supplied

A Synthesis Method to Auralize Rotor Noise During Transitional Flight

This paper describes a new capability to synthesize Urban Air Mobility (UAM) vehicle rotor/propeller loading and thickness noise and broadband self noise for transitional flight auralization. A vehicle smoothly transitioning between flight states is approximated here as transitioning in time through a series of trimmed steady-state flight conditions. To generate information that helps synthesize loading and thickness noise for transitional flight, this paper describes the development of a tool that combines blade loading, motion, and geometry data from a series of separate trimmed flight states. This paper also describes a method to combine broadband self noise acoustic predictions, each representing a separate trimmed flight state, to synthesize broadband self noise for transitional flight. Different flight profiles of the six-passenger NASA UAM Quadrotor reference vehicle demonstrate these capabilities, with each profile specified by a series of trimmed flight states.

Auralization↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS1982

A program for assessing pilot mental state in flight simulators

A program to apply proposed measures of pilot mental state in the simulator flight deck environment has been undertaken at NASA Langley Research Center. Literature survey identified a promising subset of methods for assessing workload, attention and vigilance, and task-related stress, and revealed a need for improved analytical techniques in the physiological area. An effort to apply functional modelling techniques has been initiated. A psychophysiological laboratory has been designed for simulator application. The program is designed to develop improved techniques for evaluating simulator cue fidelity and for imposing realistic workload and attentional demands on the simulator pilot.

Pope, A. T.↗
NASA NTRS2014

Ground Data System Analysis Tools to Track Flight System State Parameters for the Mars Science Laboratory (MSL) and Beyond

Flight software parameters enable space mission operators fine-tuned control over flight system configurations, enabling rapid and dynamic changes to ongoing science activities in a much more flexible manner than can be accomplished with (otherwise broadly used) configuration file based approaches. The Mars Science Laboratory (MSL), Curiosity, makes extensive use of parameters to support complex, daily activities via commanded changes to said parameters in memory. However, as the loss of Mars Global Surveyor (MGS) in 2006 demonstrated, flight system management by parameters brings with it risks, including the possibility of losing track of the flight system configuration and the threat of invalid command executions. To mitigate this risk a growing number of missions have funded efforts to implement parameter tracking parameter state software tools and services including MSL and the Soil Moisture Active Passive (SMAP) mission. This paper will discuss the engineering challenges and resulting software architecture of MSL's onboard parameter state tracking software and discuss the road forward to make parameter management tools suitable for use on multiple missions.

Allard, Dan↗
NASA NTRS2022

NDARC - NASA Design and Analysis of Rotorcraft: Input and Data Structures - Vol 2

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗
NASA NTRS2022

NDARC - NASA Design and Analysis of Rotorcraft: Input - Vol 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗
NASA NTRS2023

NDARC - NASA Design and Analysis of Rotorcraft: Input - Appendix 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗
NASA NTRS2019

Onboard Decision-Making for Nominal and Contingency sUAS Flight

This study presents an onboard decision-making architecture for small unmanned aerial systems (sUAS). The decision-maker is part of NASA's SAFE50 project that is working under the UAS Traffic Management (UTM) Technical Capability Level (TCL) 4 to provide autonomous point-to-point UAV flight in BVLOS, high-density urban environments. The decision-maker monitors various metrics to determine the safety and feasibility of the mission and categorizes flight states as Nominal, Off-Nominal, Alternate Land, and Land Now in a finite state machine. Changes in the monitored metrics serve as transitions in the state machine and trigger replanning. Navigation degradation and communication failure are simulated to show the feasibility of the decision-maker framework in appropriately switching the flight state.

Baculi, Joshua↗
NASA NTRS1987

An advisory system for predicting and resolving airspace violations based on four-dimensional guidance techniques

New heuristics are integrated with four-dimensional guidance techniques developed by NASA in order to automate the prediction and resolution of airspace violations, and three rules are derived to reduce the amount of computation time for conflict detection. Controller experience has been used to establish techniques for resolving potential conflicts including speed control, altitude maneuvers, and horizontal flight maneuvers. Current aircraft states, flight plans, and extrapolation of aircraft trajectories are used to synthesize four-dimensional trajectories, and the state of the air traffic and the interaction between aircraft are projected into the future using the four-dimensional algorithms. The situation is immediately updated to incorporate any new information. Potential conflicts among multiple aircraft are resolved by fast sequential resolution of potential conflicts between aircraft pairs, one at a time.

Lee, Homer Q.↗
NASA NTRS1989

STS crew escape for steady state gliding flight

The Space Transportation System goal of return to safe flight required that the Orbiter vehicle provide the crewmembers with a means of escape during controlled gliding flight. Because of its simplicity and safety the pole escape concept was selected for use during crew escape. To show that this concept would work in the Orbiter, wind tunnel tests, flight testing, and analytical simulation of the pole concept were pursued. This paper addresses each of these activities, showing how the wind tunnel test demonstrated the feasibility of the concept, how the flight testing was used to extract crew aero and Orbiter clearance margins, and how analytical simulations were used to verify Orbiter clearance margins. The analytical assessment of the Orbiter clearance margins shows that the pole concept will provide a minimum of 7-ft dispersed clearance for the NASA astronaut corps.

Millard, S. W.↗
NASA NTRS1995

Sensitivity of engine-integrated waverider performance to static margin constraint

The performance of an engine-integrated wedge caret-wing waverider is optimized with respect to a set of geometric and flight attitude variables with constraints for steady state flight and static margin. The optimization is done with different values for the constraint on the static margin, and the resulting performances and geometries compared. This comparison is done for three performance parameters: L/D ratio, range coefficient, and a parameter that measures energy height gain per unit weight of fuel. The vehicle model incorporates a one-dimensional calculation on the forebody, inlet and wings. Fuel injection is done prior to the combustor entrance and flow properties are calculated with a one-dimensional model using a correlation for the shock shape in front of a transverse jet. The combustor is modeled as a shock-induced-combustion scramjet, and the nozzle flow field is calculated using the method of characteristics.

Tarpley, C.↗
NASA NTRS2008

Rotorcraft Technology for HALE Aeroelastic Analysis

Much of technology needed for analysis of HALE nonlinear aeroelastic problems is available from rotorcraft methodologies. Consequence of similarities in operating environment and aerodynamic surface configuration. Technology available - theory developed, validated by comparison with test data, incorporated into rotorcraft codes. High subsonic to transonic rotor speed, low to moderate Reynolds number. Structural and aerodynamic models for high aspect-ratio wings and propeller blades. Dynamic and aerodynamic interaction of wing/airframe and propellers. Large deflections, arbitrary planform. Steady state flight, maneuvers and response to turbulence. Linearized state space models. This technology has not been extensively applied to HALE configurations. Correlation with measured HALE performance and behavior required before can rely on tools.

Young, Larry↗
NASA NTRS2016

NDARC NASA Design and Analysis of Rotorcraft - Input, Appendix 4

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description consists of all the information, input and derived, that denes the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions. The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for anti-torque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude.

Rotorcraft Input↗
NASA NTRS1980

A flight investigation of blade section aerodynamics for a helicopter main rotor having NLR-1T airfoil sections

A flight investigation was conducted using a teetering-rotor AH-1G helicopter to obtain data on the aerodynamic behavior of main-rotor blades with the NLR-1T blade section. The data system recorded blade-section aerodynamic pressures at 90 percent rotor radius as well as vehicle flight state, performance, and loads. The test envelope included hover, forward flight, and collective-fixed maneuvers. Data were obtained on apparent blade-vortex interactions, negative lift on the advancing blade in high-speed flight and wake interactions in hover. In many cases, good agreement was achieved between chordwise pressure distributions predicted by airfoil theory and flight data with no apparent indications of blade-vortex interactions.

Morris, C. E. K., Jr.↗
NASA NTRS1979

A flight investigation of basic performance characteristics of a teetering-rotor attack helicopter

Flight data were obtained with an instrumented AH-16 helicopter having uninstrumented, standard main-rotor blades. The data are presented to facilitate the analysis of data taken when the same vehicle was flown with instrumented main-rotor blades built with new airfoils. Test results include data on performance, flight-state parameters, pitch-link loads and blade angles for level flight, descending turns and pull-ups. Flight test procedures and the effects of both trim variations and transient phenomena on the data are discussed.

Morris, C. E. K., Jr.↗