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At least 955 records · Page 53

Flight-determined derivatives and dynamic characteristics of the CV-990 airplane

Flight-determined longitudinal and lateral-directional stability and control derivatives are presented for the CV-990 airplane for various combinations of Mach number, altitude, and flap setting throughout the flight envelope up to a Mach number of 0.87. Also presented are the dynamic characteristics of the aircraft calculated from the flight-obtained derivatives and the measured phugoid characteristics. The derivative characteristics were obtained from flight records of longitudinal and lateral-directional transient oscillation maneuvers by using a modified Newton-Raphson digital derivative determination technique. Generally the derivatives exhibited consistent variation with lift coefficient in the low-speed data and with Mach number and altitude in the high-speed data. Many also varied with flap deflection, notably spoiler effectiveness and directional stability.

Gilyard, G. B.↗

Modern fluid dynamics of supersonic and hypersonic flight

Many aspects of fluid dynamics impacting supersonic and hypersonic flight are examined. Progress, current problem areas, and prognostications for the future are discussed, with special emphasis on those fluid dynamics facets which can be more directly tied to a potential end-product vehicle. Numerous illustrative examples are included.

Trimpi, R. L.↗

Dynamic assertion testing of flight control software

An experiment in using assertions to dynamically test fault tolerant flight software is described. The experiment showed that 87% of typical errors introduced into the program would be detected by assertions. Detailed analysis of the test data showed that the number of assertions needed to detect those errors could be reduced to a minimal set. The analysis also revealed that the most effective assertions tested program parameters that provided greater indirect (collateral) testing of other parameters.

Andrews, D. M.↗

Magnetospheric Multiscale Mission Attitude Dynamics: Observations from Flight Data

The NASA Magnetospheric Multiscale mission, launched on Mar. 12, 2015, is flying four spinning spacecraft in highly elliptical orbits to study the magnetosphere of the Earth. Extensive attitude data is being collected, including spin rate, spin axis orientation, and nutation rate. The paper will discuss the various environmental disturbance torques that act on the spacecraft, and will describe the observed results of these torques. In addition, a slow decay in spin rate has been observed for all four spacecraft in the extended periods between maneuvers. It is shown that this despin is consistent with the effects of an additional disturbance mechanism, namely that produced by the Active Spacecraft Potential Control devices. Finally, attitude dynamics data is used to analyze a micrometeoroid/orbital debris impact event with MMS4 that occurred on Feb. 2, 2016.

Conjunction↗

Soaring flight and the Rhon contests

Explanation of soaring flight. Static and dynamic soaring flight. Results of the Rhon contest. Description of the most important gliders. Notes on Soaring Flight Contests in France and England.

Hoff, Wilhelm↗

Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

Corker, Kevin M.↗

SAFER Inspection of Space Shuttle Thermal Protection System

In the aftermath of the space shuttle Columbia accident, it quickly became clear that new methods would need to be developed that would provide the capability to inspect and repair the shuttle's thermal protection system (TPS). A boom extension to the Remote Manipulator System (RMS) with a laser topography sensor package was identified as the primary means for measuring the damage depth in acreage tile as well as scanning Reinforced Carbon- Carbon (RCC) surfaces. However, concern over the system's fault tolerance made it prudent to investigate alternate means of acquiring close range photographs and contour depth measurements in the event of a failure. One method that was identified early was to use the Simplified Aid For EVA Rescue (SAFER) propulsion system to allow EVA access to damaged areas of concern. Several issues were identified as potential hazards to SAFER use for this operation. First, the ability of an astronaut to maintain controlled flight depends upon efficient technique and hardware reliability. If either of these is insufficient during flight operations, a safety tether must be used to rescue the crewmember. This operation can jeopardize the integrity of the Extra-vehicular Mobility Unit (EMU) or delicate TPS materials. Controls were developed to prevent the likelihood of requiring a tether rescue, and procedures were written to maximize the chances for success if it cannot be avoided. Crewmember ability to manage tether cable tension during nominal flight also had to be evaluated to ensure it would not negatively affect propellant consumption. Second, although propellant consumption, flight control, orbital dynamics, and flight complexity can all be accurately evaluated in Virtual Reality (VR) Laboratory at Johnson Space Center, there are some shortcomings. As a crewmember's hand is extended to simulate measurement of tile damage, it will pass through the vehicle without resistance. In reality, this force will push the crewmember away from the vehicle, and could induce a moment which, if strong enough, could saturate the attitude control system in SAFER. This raises the concern that additional propellant will be consumed to maintain controlled flight. To account for this, the fidelity of the Virtual Reality simulation was improved to include the effect of crewmember contact with the vehicle during SAFER flight. In addition, while participating in VR simulations, the subject is in shirt sleeves and sits in a chair. This does not provide a flight-like representation of body position awareness. To prevent inadvertent contact with tile or RCC, other facilities were utilized to establish crew preferences for body attitude and tool configuration. Finally, a study was performed to determine if attitude constraints are needed for the Space shuttle and International Space Station to reduce SAFER flight difficulty.

Scoville, Zebulon C.↗

Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms

Dynamic environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal paths through these environments. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially with the number of agents in the system. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance.

Coevolution↗

Human-in-the-Loop Evaluation of Dynamic Multi-Flight Common Route Advisories

Flights often experience large delays when they are routed around weather. Multi-flight common route advisories provide delay recovery by suggesting time-saving re-routes for groups of flights whose current weather-avoidance routes have become outdated because the weather has dissipated and/or moved away. A laboratory evaluation of these advisories was conducted by four subject matter experts having extensive experience in traffic flow management operations. These experts provided a total of 120 data points in the airspace of Houston Center. The multi-flight common route tool provides time-saving route change advisories taking into account flight plans, wind fields, and the spatio-temporal evolution of predicted convective weather. It is not designed to account for complex operational factors such as non-standard sector traversal and interactions with local traffic management initiatives; hence a relatively low percentage (37%) of advisories generated by the tool were rated as acceptable. However, a high percentage (81%) of advisories were rated as acceptable after the subject matter experts used the tool's user interface to make route modifications that accounted for relevant operational factors not considered by the tool. The workload associated with using the tool, as measured by the NASA Task Load Index, was quite low (1.1 on a scale of 0 to 10). The results of this evaluation make a good case for human-automation teaming to design operationally valid weather re-routes for delay recovery.

Traffic Flow Management↗

Human-in-the-Loop Evaluation of Dynamic Multi-Flight Common Route Advisories

Flights often experience large delays when they are routed around weather. Multi-flight common route advisories provide delay recovery by suggesting time-saving re-routes for groups of flights whose current weather-avoidance routes have become outdated because the weather has dissipated and/or moved away. A laboratory evaluation of these advisories was conducted by four subject matter experts having extensive experience in traffic flow management operations. These experts provided a total of 120 data points in the airspace of Houston Center. The multi-flight common route tool provides time-saving route change advisories taking into account flight plans, wind fields, and the spatio-temporal evolution of predicted convective weather. It is not designed to account for complex operational factors such as non-standard sector traversal and interactions with local traffic management initiatives; hence a relatively low percentage (37%) of advisories generated by the tool were rated as acceptable. However, a high percentage (81%) of advisories were rated as acceptable after the subject matter experts used the tool's user interface to make route modifications that accounted for relevant operational factors not considered by the tool. The workload associated with using the tool, as measured by the NASA Task Load Index, was quite low (1.1 on a scale of 0 to 10). The results of this evaluation make a good case for human-automation teaming to design operationally valid weather re-routes for delay recovery.

Traffic Flow Management↗