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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.

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.

Two versions of a prototype Free Flight cockpit situational display (Basic and Enhanced) were examined in a simulation at the NASA Ames Research Center. Both displays presented a display of traffic out to a range of 120 NM, and an alert when the automation detected a substantial danger of losing separation with another aircraft. The task for the crews was to detect and resolve threats to separation posed by intruder aircraft. An Enhanced version of the display was also examined. It incorporated two additional conflict alerting levels and tools to aid in trajectory prediction and path planning. Ten crews from a major airline participated in the study. Performance analyses and pilot debriefings showed that the Enhanced display was preferred, and that minimal separation between the intruder and the ownship was larger with the Enhanced display. In addition, the additional information on the Enhanced display did not lead crews to engage in more maneuvering.

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.

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 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.

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 cognitive abilities of some astronauts are affected during spaceflight. We investigated whether a simulated space flight ascent environment, including vibration and 3.8 Gx ascent forces, would result in cognitive deficits detectable by the WinSCAT test battery. Eleven participants were administered the computerized cognitive test battery, a workload rating questionnaire and a subjective state questionnaire before and after a combination of acceleration plus vibration conditions. The acceleration plus vibration exposure resulted in significant self-reports of physical discomfort but did not significantly affect cognitive test battery scores. We discuss ways in which a cognitive assessment tool could be made more sensitive to subtle cognitive changes relevant to astronaut performance.

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.

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.

Design, operations and maintenance activities in aviation involve analysis of variety of aviation data. This data is typically in disparate formats making it difficult to use with different software packages. Use of a self-describing and extensible standard called XML provides a solution to this interoperability problem. While self-describing nature of XML makes it easy to reuse, it also increases the size of data significantly. A natural solution to the problem is to compress the data using suitable algorithm and transfer it in the compressed form. We found that XML-specific compressors such as Xmill and XMLPPM generally outperform traditional compressors. However, optimal use of Xmill requires of discovery of optimal options to use while running Xmill. Manual discovery of optimal setting can require an engineer to experiment for weeks.

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.

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.

Remote Tower Sensor Systems are proof-of-concept prototypes being developed by NASA/Ames Research Center (NASA/ARC) with collaboration with the FAA and NOAA. RTSS began with the deployment of an Airport Approach Zone Camera System that includes real-time weather observations at San Francisco International Airport. The goal of this research is to develop, deploy, and demonstrate remotely operated cameras and sensors at several major airport hubs and un-towered airports. RTSS can provide real-time weather observations of airport approach zone. RTSS will integrate and test airport sensor packages that will allow remote access to real-time airport conditions and aircraft status.

The animal habitat under development for the International Space Station (ISS) provides a unique opportunity for the physiological and biological science community to perform controlled experiments in microgravity on rats and mice. This paper discusses the complexities that arise in developing a new animal habitat to be flown aboard the ISS. Such development is incremental and moves forward by employing the past successes, learning from experienced shortcomings, and utilizing the latest technologies. The standard vivarium cage on the ground can be a very simple construction, however the habitat required for rodents in microgravity on the ISS is extremely complex. This discussion presents an overview of the system requirements and focuses on the unique scientific and engineering considerations in the development of the controlled animal habitat parameters. In addition, the challenges to development, specific science, animal welfare, and engineering issues are covered.