Search Results

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-in-trail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011.

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

This paper features a set of advanced technologies for autonomy and intelligence in advanced inspection systems of facility operations. These technologies offer a significant contribution to set a path to establish a system and an operating environment with autonomy and intelligence for inspection, monitoring and safety via gas and ambient sensors, video mining and speech recognition commands on unmanned ground vehicles and other platforms to support operational activities in the Cryogenics Test bed and other facilities and vehicles. These advanced technologies are in current development and progress and their functions and operations require guidance and formulation in conjunction with the development team(s) toward the system architecture.

This paper offers a concept for a regenerable, low-power system for purifying exhaust from a solid waste processor. The innovations in the concept include the use of a closed-loop regeneration cycle for the adsorber, which prevents contaminants from reaching the breathable air before they are destroyed, and the use of a humidity-swing desorption cycle, which uses less power than a thermal desorption cycle and requires no venting of air and water to space vacuum or planetary atmosphere. The process would also serve well as a trace contaminant control system for the air in the closed environment. A systems-level design is presented that shows how both the exhaust and air purification tasks could be performed by one processor. Data measured with a fixed-bed apparatus demonstrate the effects of the humidity swing on regeneration of the adsorbent.

The Plant Generic BioProcessing Apparatus (PGBA), a plant growth facility developed for commercial space biotechnology research, has flown successfully on 3 spaceflight missions for 4, 10 and 16 days. The environmental control systems of this plant growth chamber (28 liter/0.075 m2) provide atmospheric, thermal, and humidity control, as well as lighting and nutrient supply. Typical performance profiles of water transpiration and dehumidification, carbon dioxide absorption (photosynthesis) and respiration rates in the PGBA unit (on orbit and ground) are presented. Data were collected on single and mixed crops. Design options and considerations for the different sub-systems are compared with those of similar hardware.

The Rotating Disk Analytical System (R-DAS) is an in-situ, bio-analytical technology, which utilizes a micro-fluidic disk with similar form factor as an audio compact disc to enhance and augment microgravity-based cellular and molecular biology research. The current micro-fluidic assay performs live cell/dead cell analysis using fluorescent microscopy. Image acquisition and analysis are performed for each of the selected microscope slide windows. All images are stored for later download and possible further post analysis. The flight version of the R-DAS will occupy a double mid-deck shuttle locker or one quarter of an ISS rack. The control system for the R-DAS consists of a set of interactive software components. These components interact with one another to control disk rotation, vertical and horizontal stage motion, sample incubation, image acquisition and analysis, and human interface.

The Elytron 2S is a prototype aircraft concept to allow VTOL capabilities together with fixed wing aircraft performance. It has a box wing design with a centrally mounted tilt-wing supporting two rotors. This paper explores the aerodynamic characteristics of the aircraft using computational fluid dynamics in hover and low speed forward flight, as well as analyzing the unique control system in place for hover. The results are then used to build an input set for NASA Design and Analysis if Rotorcraft software allowing trim and flight stability and control estimations to be made with SIMPLI-FLYD.

Pyrolysis is a very versatile waste processing technology which can be tailored to produce a variety of solid liquid and/or gaseous products. The main disadvantages of pyrolysis processing are: (1) the product stream is more complex than for many of the alternative treatments; (2) the product gases cannot be vented directly into the cabin without further treatment because of the high CO concentrations. One possible solution is to combine a pyrolysis step with catalytic oxidation (combustion) of the effluent gases. This integration takes advantage of the best features of each process, which is insensitivity to product mix, no O2 consumption, and batch processing, in the case of pyrolysis, and simplicity of the product effluent stream in the case of oxidation. In addition, this hybrid process has the potential to result in a significant reduction in Equivalent System Mass (ESM) and system complexity.

One of the major problems associated with solid waste processing technologies is effluent contaminants that are released in gaseous forms from the processes. This is a concern in both biological as well as physicochemical solid waste processing. Carbon dioxide (CO2), the major gas released, does not present a serious problem and there are currently in place a number of flight-qualified technologies for CO2 removal. However, a number of other gases, in particular NOx, SO2, NH3, and various hydrocarbons (e.g. CH4) do present health hazards to the crew members in space habitats. In the present configuration of solid waste processing in the International Space Station (ISS), some of these gases are removed by the Trace Contaminant Control System (TCCS), demands a major resupply. Reduction of the resupply can be effective by using catalyst impregnated carbon nanotubes. For example, NO decomposition to N2 and O2 is thermodynamically favored.

The paper describes an approach to the integration of qualitative and quantitative modeling techniques for advanced life support (ALS) systems. Developing reliable control strategies that scale up to fully integrated life support systems requires augmenting quantitative models and control algorithms with the abstractions provided by qualitative, symbolic models and their associated high-level control strategies. This will allow for effective management of the combinatorics due to the integration of a large number of ALS subsystems. By focusing control actions at different levels of detail and reactivity we can use faster, simpler responses at the lowest level and predictive but complex responses at the higher levels of abstraction. In particular, methods from model-based planning and scheduling can provide effective resource management over long time periods.

This paper describes a multi-disciplinary damage detection methodology that can aid in detecting and diagnosing a damage in a given structural system, not limited to the example of a rotating gear presented here. Damage detection is performed on the gear stress data corresponding to the steady state conditions. The normal and damage data are generated by a finite-difference solution of elastodynamic equations of velocity and stress in generalized coordinates1. The elastodynamic solution provides a knowledge of the stress distribution over the gear such as locations of stress extrema, which in turn can lead to an optimal placement of appropriate sensors over the gear to detect a potential damage. The damage detection is performed by a multi-function optimization that incorporates Tikhonov kernel regularization reinforced by an added Laplacian regularization term as used in semi-supervised machine learning. Damage is mimicked by reducing the rigidity of one of the gear teeth.

This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.

This paper presents a novel health monitoring and fault adaptive control architecture for an unmanned hexrotor helicopter. The technologies developed to achieve the described level of robust fault contingency management include; 1.) A Particle Swarm Optimization (PSO) routine for maximizing the “built-in” fault tolerance that the closed loop flight control system affords, 2.) A two-stage Kalman filter scheme for real-time identification of faults that are masked by control system compensation, and 3.) A reconfigurable control allocation method which compensates for large degradations of the six main motor/rotor assemblies. The fault adaptive control system presented herein has strong robustness against small faults without the need for controller reconfiguration, and strong tolerance of large faults through adaptive accommodation of the fault source and severity.

Spaceflight plant growth chambers require an atmosphere control system to maintain adequate levels of carbon dioxide and oxygen, as well as to limit trace gas components, for optimum or reproducible scientific performance. Recent atmosphere control anomalies of a spaceflight plant chamber, resulting in unstable CO2 control, have been analyzed. An activated carbon filter, designed to absorb trace gas contaminants, has proven detrimental to the atmosphere control system due to its large buffer capacity for CO2. The latest plant chamber redesign addresses the control anomalies and introduces a new approach to atmosphere control (low leakage rate chamber, regenerative control of CO2, O2, and ethylene).