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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.

The Vapor Phase Catalytic Ammonia Removal (VPCAR) technology has been previously discussed as a viable option for the Exploration Water Recovery System. This technology integrates a phase change process with catalytic oxidation in the vapor phase to produce potable water from exploration mission wastewaters. A developmental prototype VPCAR was designed, built and tested under funding provided by a National Research Announcement (NRA) project. The core technology, a Wiped Film Rotating Device (WFRD) was provided by Water Reuse Technologies under the NRA, whereas Hamilton Sundstrand Space Systems International performed the hardware integration and acceptance test of the system. Personnel at the Ames Research Center performed initial systems test of the VPCAR using ersatz solutions. To assess the viability of this hardware for Exploration Life Support (ELS) applications, the hardware has been modified and tested at the MSFC ECLS Test Facility.

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.

A habitat for housing up to 32 Tenebrionid, black body beetles (Trigonoscelis gigas Reitter) has been developed at Ames Research Center for conducting studies to evaluate the effects of long duration spaceflight upon insect circadian timing systems. This habitat, identified as the Beetle Kit, provides an automatically controlled lighting system and activity and temperature recording devices, as well as individual beetle enclosures. Each of the 32 enclosures in a Beetle Kit allows for ad lib movement of the beetle as well as ventilation of the beetle enclosure via an externally operated hand pump. Two Beetle Kits were launched on STS-84 (Shuttle-Mir Mission-06) on May 15, 1997 and were transferred to the Priroda module of the Russian Mir space station on May 18 as part of the NASA/Mir Phase 1 Science Program. Following the Progress collision with Spektr on June 25, the Kits were transferred to the Kristall module. The beetles will remain on Mir for approximately 135 days.

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 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.

Direct osmotic concentration (DOC) is an integrated membrane treatment process designed for the reclamation of spacecraft wastewater. The system includes forward osmosis (FO), membrane evaporation, reverse osmosis (RO) and an aqueous phase catalytic oxidation (APCO) post-treatment unit. This document describes progress in the third year of a four year project to advance hardware maturity of this technology to a level appropriate for human rated testing. The current status of construction and testing of the final deliverable is covered and preliminary calculations of equivalent system mass are funished.

The Vapor Phase Catalytic Ammonia Removal (VPCAR) Technology has undergone long duration testing at MSFC. The results of this testing revealed several areas in which the VPCAR Technology could be improved and those improvements are summarized here. These improvements include the replacement of several parts with units that are more durable, redesign of several pieces which proved to have mechanical weaknesses, and incorporation of some new designs in order to prevent other potential problems.

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.

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.

Equivalent System Mass (ESM) is used by the Advanced Life Support (ALS) community to quantify mission costs of technologies for space applications (Drysdale et al, 1999, Levri et al, 2000). Mass is used as a cost measure because the mass of an object determines propulsion (acceleration) cost (i.e. amount of fuel needed), and costs relating to propulsion dominate mission cost. Mission location drives mission cost because acceleration is typically required to initiate and complete a change in location. Total mission costs may be reduced by minimizing the mass of materials that must be propelled to each distinct location. In order to minimize fuel requirements for missions beyond low-Earth orbit (LEO), the hardware and astronauts may not all go to the same location. For example, on a Lunar or Mars mission, some of the hardware or astronauts may stay in orbit while the rest of the hardware and astronauts descend to the planetary surface.