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This paper introduces a data driven model for predicting airport arrival capacity with 2-8 hour look-ahead forecast data. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport’s arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system.

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.

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.

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.

We consider the heat transfer characteristics of an ideal concentric disk used in the Wiped-Film Rotating-Disk (WFRD) evaporator for the Vapor Phase Catalytic Ammonia Removal (VPCAR) water recovery system. A mathematical model is derived to predict the radial temperature distribution and its average over the surface of the disk as a function of system parameters. The model shows self-similarity of the temperature distribution and the existence of a dimensionless parameter S (ratio of heat flux to convection) that can be used as a criterion to optimize the thermal characteristics of the disk in order to approach uniform surface temperature. Comparison of the model to experimental data using global (infrared imager) and local (resistive temperature devices) measurements shows that agreement with the model depends on the ambient condition denoted by the local heat transfer coefficient.

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.

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.

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.

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 presents results of research on a solid waste dryer, based of the process of lyophilization, which recovers water and stabilizes solid waste. A lyophilizer has been developed and tested that uses thermoelectric heat pumps (TECs) to recycle heat during drying. The properties of TECs facilitate direct measurement of heat flow rates, and heat flow data are used to evaluate a heat and mass transfer model of the thermoelectric lyophilizer. Data are consistent with the theoretical model in most respects. Practical problems such as insulation and vacuum maintenance are minor in this system. However, the model’s assumption of a uniformly retreating ice layer during drying is valid only for the first 30% of water removed. Beyond this point, a shrinking core or lens model is more appropriate. Heat transfer to the shrinking core surrounded by dried material is slow.

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.

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 purification of waste water is a critical element of any long-duration space mission. The Vapor Phase Catalytic Ammonia Removal (VPCAR) system offers the promise of a technology requiring low quantities of expendable material that is suitable for exploration missions. NASA has funded an effort to produce an engineering development unit specifically targeted for integration into the NASA Johnson Space Center's Integrated Human Exploration Mission Simulation Facility (INTEGRITY) formally known in part as the Bioregenerative Planetary Life Support Test Complex (Bio-Plex) and the Advanced Water Recovery System Development Facility. The system includes a Wiped-Film Rotating-Disk (WFRD) evaporator redesigned with micro-gravity operation enhancements, which evaporates wastewater and produces water vapor with only volatile components as contaminants. Volatile contaminants, including organics and ammonia, are oxidized in a catalytic reactor while they are in the vapor phase.

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 study investigated pilots' taxi performance, situation awareness and workload while taxiing with three different head-up display (HUD) symbology formats: Command-guidance, Situation-guidance and Hybrid. Command-guidance symbology provided the pilot with required control inputs to maintain centerline position; Situation-guidance symbology provided conformal, scene-linked navigation information; while the Hybrid symbology combined elements of both symbologies. Taxi speed, centerline tracking accuracy, workload and situation awareness were assessed. Taxi speed, centerline accuracy, and situation awareness were highest and workload lowest with Situation-guidance and Hybrid symbologies. These results are thought to be due to cognitive tunneling induced by the Command-guidance symbology. The conformal route information of the Situation-guidance and Hybrid HUD formats provided a common reference with the environment, which may have supported better distribution of attention.