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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.

The operation of a crop growth system in micro-gravity is an important part of the National Aeronautics and Space Administration's Closed Ecological Life Support System development program. Maintaining densely arrayed plants in a closed environment imposed to induce high growth rates must be expected to result in substantial levels of water transpiration rate. Since the environmental air is recirculated, the transpiration water must be removed. In an operating CELSS, it is expected that this water will provide potable water for use of the crew. There is already considerable knowledge about water removal from crew environmental air during orbital and transfer activities, and the difference between the conditions of the described requirement and the conditions for which experience has been gained is the quantities involved and the reliability implications due to the required periods of operation.

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.

Waste management is a vital function of spacecraft life support systems as it is necessary to meet crew health and safety and quality of life requirements. Depending on the specific mission requirements, waste management operations can include waste collection, segregation, containment, processing, storage and disposal. For the Crew Exploration Vehicle (CEV), addressing volume and mass constraints is paramount. Reducing the volume of trash prior to storage is a viable means to recover habitable volume, and is therefore a particularly desirable waste management function to implement in the CEV, and potentially in other spacecraft as well. Research is currently being performed at NASA Ames Research Center to develop waste compaction systems that can provide both volume and mass savings for the CEV and other missions.

We present a device for collecting and storing feces in microgravity that is user-friendly yet suitable for spacecraft in which cabin volume and mass are constrained. On Apollo missions, the commode function was served using disposable plastic bags, which proved time-consuming and caused odor problems. On Skylab, the space shuttle, and the International Space Station, toilets have used airflow beneath a seat to control odors and collect feces. We propose to incorporate airflow into a system of self-compacting, self-drying collection and stowage bags, providing the benefits of previous commodes while minimizing mass and volume. Each collection bag consists of an inner layer of hydrophobic membrane that is permeable to air but not liquid or solid waste, an outer layer of impermeable plastic, and a collapsible spacer separating the inner and outer layers. Filled bags are connected to space vacuum, compacting and drying their contents.

The initial concepts and construction of a three layered, water-absorbent, zero-G, compactor trash bag will be described. This bag is composed of an inner wicking layer, a middle absorbent layer, and an outer containment layer. The primary properties of the wicking layer are the fast adsorption of any free liquid released within the trash bag and the lateral spreading of this liquid around the interior of the bag. The absorbent layer sequesters and stores the liquid captured by the wicking layer. It need not be as fast acting as the wicking layer, but has to have a much larger capacity. The containment layer allows for handling of the bag without worry of releasing the contents. The combined strength of the three layers needs to be sufficient to withstand the forces exerted by the compactor.

Some selected segments of the ascent and the on-orbit data from the Space Shuttle flight, STS114, as well as some selected laboratory test article data have been analyzed using wavelets, power spectrum and autocorrelation function. Additionally, a simple approximate noise test was performed on these data segments to confirm the presence or absence of white noise behavior in the data. This study was initially directed at characterizing the on-orbit background against which a signature due to an impact during on-orbit operation could be identified. The laboratory data analyzed here mimic low velocity impact that the Orbiter may be subjected to during the very initial stages of ascent.

Linear regression is the primary data analysis method used in the development of air traffic delay models. When the data being studied does indeed have an underlying linear model, this approach would produce the best-fitting model as expected. However, it has been argued by ATM researchers [Wieland2005, Evans2004] that the underlying delay models are primarily non-linear. Furthermore, the delays being modeled often depend not only on the observable independent variables being studied but also on other variables not being considered. The traditional regression approach alone may not be best suited to study these type of problems. In this paper, we propose an alternate methodology based on partitioning the data using statistical and decision tree learning methods. We then show the utility of this model in a variety of different ATM modeling problems.

This study examines the current state of the art in rodent habitats as well as the next generation of rodent habitats currently under development at NASAs Ames Research Center. Space Shuttle missions are currently limited in duration to just over two weeks. In contrast to this, future life science missions aboard the Space Station may last months or even years. This will make resource conservation and utilization critical issues in the development of rodent habitats for extended microgravity missions. Emphasis is placed on defining rodent requirements for extended space flights of up to 90 days, and on improving habitability and extending the useful performance life of rodent habitats.

An innovative design that meets both Shuttle and Space Station requirements for a user-friendly, volume-efficient, portable glovebox system has been developed at Ames Research Center (ARC). The Standard Interface Glovebox (SIGB) has been designed as a two Middeck locker-sized system that mounts in a Middeck Rack Structure (MRS) or in any rack using the Standard Interface Rack (SIR) rail spacing. The MRS provides structural support for the SIGB during all aspects of the mission and is an interface consistent with NASA's desire for commonality of mechanical interfaces, allowing the SIGB to be flown on essentially any manned space platform. The SIGB provides an enclosed work volume which operates at negative pressure relative to ambient, as well as excellent lighting and ample work volume for anticipated life sciences-related experiment operations inflight.

The reproductive ontogeny of ‘Super-Dwarf’ wheat grown on the space station Mir is chronicled from the vegetative phase through flower' development. Changes in the apical meristem associated with transition from the vegetative plhase to floral initiation and development of the reproductive spike were all typical of ‘Super Dwarf’ wheat up to the point of anthesis. Filament elongation, which characteristically occurs just prior to anthesis (during floral development stage 4) and moves the anthers through the stigmatic branches thus facilitating pollination, did not occur in the flowers of spikes grown on Mir. While pollen did form in the anthers, no evidence of pollination or fertilization was observed. Analysis of pollen idlentified abnormalities; the presence of only one nucleus in the pollen as opposed to the normal trinucleate condition is likely an important factor in the sterility observed in wheat grown on Mir.

Training to operate and manage Constellation vehicles, which include a crewed spacecraft and the lunar lander, is an essential part of the Constellation program. This paper discusses the on-going preparations for a Constellation Training Facility (CxTF). CxTF will be compromised of training simulators that will be used, in part, to prepare crew and flight controllers for vehicle operations. Current training simulators are reviewed to identify and outline key CxTF elements, i.e., part-task and full-task trainers. These trainers are further discussed within the context of the Constellation missions.

The “low power-CO2 removal (LPCOR) system” is an advanced air revitalization system that is under development at NASA Ames Research Center. The LPCOR utilizes the fundamental design features of the ‘four bed molecular sieve’ (4BMS) CO2 removal technology of the International Space Station (ISS). LPCOR improves power efficiency by replacing the desiccant beds of the 4BMS with a membrane dryer and a state-of-the-art, structured adsorbent device that collectively require 25% of the thermal energy required by the 4BMS desiccant beds for regeneration. Compared to the 4BMS technology, it has the added functionality to deliver pure, compressed CO2 for oxygen recovery. The CO2 removal and recovery functions are performed in a two-stage adsorption compressor. CO2 is removed from the cabin air and partially compressed in the first stage. The second stage performs further compression and delivers the compressed CO2 to a reduction unit such as a Sabatier reactor for oxygen recovery.

The Mission Operations Directorate (MOD) of the Johnson Space Center is responsible for providing continuous operations support for the International Space Station (ISS). Operations support requires flight controllers who are skilled in team performance as well as the technical operations of the ISS. ISS flight controller certification has evolved to include a balanced focus on the development of team performance and technical expertise. The latest challenge the ISS team faces is how to certify an ISS flight controller to the required level of effectiveness in one year. Space Flight Resource Management (SFRM) training, a NASA adapted variant of Crew Resource Management (CRM), is expanding the role of senior flight controllers as mentors to help meet that challenge. This paper explains our mentoring approach and discusses its effectiveness and future applicability in promoting SFRM/CRM skills.

The Flexible Membrane Commode (FMC) is an alternative waste management system designed to address the severe mass restrictions on the Orion vehicle. The concept includes a deployable seat and single use, three layer bags that employ air flow to draw solids away from the body and safely contain them in disposable bags.1 Simulated microgravity testing of the system was performed during two separate parabolic flight campaigns in July and August of 2008. Experimental objectives included verifying the waste fill procedures in reduced gravity, characterizing waste behavior during the filling process, and comparison of the results with model predictions. In addition the operational procedure for bag installation, removal, and sealing were assessed. 2 A difficult operational requirement concerns the delivery of the fecal waste simulant into the upper area of the bag in a manner that faithfully simulates human defecation.

Two separate experimental rigs used in tests on NASA and Zero-G Corporation aircrafts flying low-gravity trajectories, and in the NASA 2.2 Second Drop Tower have been developed to test the functioning of the Flexible Membrane Commode (FMC) concept under reduced gravity conditions. The first rig incorporates the flexible, optically opaque membrane bag and the second rig incorporates a transparent chamber with a funnel assembly for evacuation that approximates the size of the membrane bag. Different waste dispensers have been used including a caulking gun and flexible hose assembly, and an injection syringe. Waste separation mechanisms include a pair of wire cutters, an iris mechanism, as well as discrete slug injection. The experimental work is described in a companion paper. This paper focuses on the obtained results and analysis of the data.

Aviation training has remained largely untouched by decades of development in cognitive science. In aviation, people must be trained to perform complicated tasks and make good operational decisions in complex dynamic environments. However, traditional approaches to professional aviation training are not well designed to accomplish this goal. Aviation training has been based mainly on relatively rigid classroom teaching of factual information followed by on-the-job mentoring. This approach tends to compartmentalize knowledge. It is not optimal for teaching operational decision-making, and it is costly in time and personnel. The effectiveness of training can be enhanced by designing programs that support the psychological processes involved in learning, retention, retrieval, and application. By building programs that are informed by current work in cognitive science and that utilize modern technological advances, efficient training programs can be created.

This paper describes recent work on developing an extensible information grid for risk management at NASA — a RISK INFORMATION GRID. This grid is being developed by integrating information grid technology with risk management processes for a variety of risk related applications. To date, RISK GRID applications are being developed for three main NASA processes: risk management — a closed-loop iterative process for explicit risk management, program/project management — a proactive process that includes risk management, and mishap management — a feedback loop for learning from historical risks that ‘escaped’ other processes. This is enabled through an architecture involving an extensible database, structuring information with XML, ‘schema-less’ mapping of XML, and secure server-mediated communication using standard protocols.

A number of airlines have FOQA programs that analyze archived flight data. Although this analysis process is extremely useful for assessing airline concerns in the areas of aviation safety, operations, training, and maintenance, looking at flight data in isolation does not always provide the context necessary to support a comprehensive analysis. To improve the analysis process, the Aviation Data Integration Project (ADIP) has been developing techniques for integrating flight data with auxiliary sources of relevant aviation data. ADIP has developed an aviation data integration system (ADIS) comprised of a repository and associated integration middleware that provides rapid and secure access to various data sources, including weather data, airport operating condition (ATIS) reports, radar data, runway visual range data, and navigational charts.