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The development of virtual environment technology at NASA Ames Research Center and other research institutions has created opportunities for enhancing human performance. The application of this technology to training astronaut flight crews planning to go onboard the International Space Station has already begun at the NASA Johnson Space Center. A unique application of virtual environments to crew training is envisioned at NASA Ames Research Center which combines state of the art technology with haptic feedback to create a method for training crewmembers on critical life sciences operations which require fine motor skills. This paper describes such a concept, known as the Virtual Glovebox, as well as surveys other applications of virtual environments to astronaut crew training.

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.

The short takeoff and landing capabilities that characterize the performance of powered-lift aircraft are dependent on engine thrust and are, therefore, severely affected by loss of an engine. This paper shows that the effects of engine loss on the short takeoff and landing performance of powered-lift aircraft can be effectively mitigated by cross-shafting the engine fans in a twin-engine configuration. Engine-out takeoff and landing performances are compared for three powered-lift aircraft configurations: one with four engines, one with two engines, and one with two engines in which the fans are cross-shafted. The results show that the engine-out takeoff and landing performance of the cross-shafted two-engine configuration is significantly better than that of the two-engine configuration without cross-shafting.

The Controlled Environment Research Chamber (CERC) at the NASA Ames Research Center was created for early-on investigation of promising new technologies for life support of advanced space exploration missions. The CERC facility is being used to address the advanced technology requirements necessary to implement an integrated working and living environment for a planetary habitat. The CERC, along with a human-powered centrifuge, a planetary terrain simulator, advanced displays, and a virtual reality capability, is able to develop and demonstrate applicable technologies for future planetary exploration. There will be several robotic mechanisms performing exploration tasks external to the habitat that will be controlled through the virtual environment to provide representative workloads for the crew.

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.

NASA is exploring a research activity to identify the technologies that will enable an Extreme Short Take-Off and Landing (ESTOL) aircraft. ESTOL aircraft have the potential to offer a viable solution to airport congestion, delay, capacity, and community noise concerns. This can be achieved by efficiently operating in the underutilized or unused airport ground and airspace infrastructure, while operating simultaneously, but not interfering with, conventional air traffic takeoffs and landings. Concurrently, the Air Force is exploring ESTOL vehicle solutions in the same general performance class as the NASA ESTOL vehicle to meet a number of Advanced Air Mobility missions. The capability goals of both the military and civil vehicles suggests synergistic technology development benefits. This paper presents a summary of the activities being supported by the NASA ESTOL Vehicle Sector.

Various methods have been used to simulate reduced gravity environments for space systems research and development. Neutral buoyancy has been the most universally used simulation of zero-g. This paper describes the facilities, personnel and experimental work that are associated with the Neutral Buoyancy Test Facility (NBTF) at NASA Ames Research Center (ARC). This facility provides a unique underwater environment for the researcher to simulate reduced gravity activities and evaluate the performance of space-related equipment. The NBTF's small size gives it several advantages over larger water facilities. First, a smaller crew ensures a lower overhead. Second, the facility is used for research purposes only, eliminating any scheduling conflicts with astronaut training. Lastly, the small volume of water allows the researcher to more easily vary the water temperature. This feature is ideal for investigations of astronaut thermal comfort and regulation.

To fully conduct research that will support the far-term concepts, technologies and methods required to improve the safety of Air Transportation a simulation environment of the requisite degree of fidelity must first be in place. The Virtual National Airspace Simulation (VNAS) will provide the underlying infrastructure necessary for such a simulation system. Aerospace-specific knowledge management services such as intelligent data-integration middleware will support the management of information associated with this complex and critically important operational environment. This simulation environment, in conjunction with a distributed network of super-computers, and high-speed network connections to aircraft, and to Federal Aviation Administration (FAA), airline and other data-sources will provide the capability to continuously monitor and measure operational performance against expected performance.

Propulsion system sizing for mechanical flap and externally blown flap aircraft is demonstrated. Included in this study is the effect of various levels of noise suppression on the aircraft final design characteristics. Both aircraft are sized to operate from a 3000 ft runway and perform the same mission. For each aircraft concept, propulsion system sizing is demonstrated for two different engine cycles-one having a fan pressure ratio of 1.5 and a bypass ratio of 9 and the other having a fan pressure ratio of 1.25 and a bypass ratio of 17.8. The results presented include the required thrust to weight ratio, wing loading, resulting gross weight and direct operating costs as functions of the engine noise level for each combination of engine cycle and aircraft concept.

This paper describes research and development for reducing the aerodynamic drag of heavy vehicles by demonstrating new approaches for the numerical simulation and analysis of aerodynamic flow. In addition, greater use of newly developed computational tools holds promise for reducing the number of prototype tests, for cutting manufacturing costs, and for reducing overall time to market. Experimental verification and validation of new computational fluid dynamics methods are also an important part of this approach. Experiments on a model of an integrated tractor-trailer are underway at NASA Ames Research Center and the University of Southern California. Companion computer simulations are being performed by Sandia National Laboratories, Lawrence Livermore National Laboratory, and California Institute of Technology using state-of- the-art techniques, with the intention of implementing more complex methods in the future.

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 On-line Project Information System (OPIS) is the Exploration Life Support (ELS) mechanism for task data sharing and annual reporting. Fiscal year 2008 (FY08) was the first year in which ELS Principal Investigators (PI's) were required to complete an OPIS annual report. The reporting process consists of downloading a template that is customized to the task deliverable type(s), completing the report, and uploading the document to OPIS for review and approval. In addition to providing a general status and overview of OPIS features, this paper describes the user critiques and resulting system modifications of the first year of OPIS reporting efforts. Specifically, this paper discusses process communication and logistics issues, user interface ambiguity, report completion challenges, and the resultant or pending system improvements designed to circumvent such issues for the fiscal year 2009 reporting effort.

A review of the research accomplished in 2009 in the System-Level Design, Analysis and Simulation Tools (SLDAST) of the NASA's Airspace Systems Program is presented. This research thrust focuses on the integrated system-level assessment of component level innovations, concepts and technologies of the Next Generation Air Traffic System (NextGen) under research in the ASP program to enable the development of revolutionary improvements and modernization of the National Airspace System. The review includes the accomplishments on baseline research and the advancements on design studies and system-level assessment, including the cluster analysis as an annualization standard of the air traffic in the U.S. National Airspace, and the ACES-Air MIDAS integration for human-in-the-loop analyzes within the NAS air traffic simulation.

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.

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.

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 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 elemental composition of food consumed by astronauts is well defined. The major elements carbon, hydrogen, oxygen, nitrogen and sulfur are taken up in large amounts and these are often associated with the organic fraction (carbohydrates, proteins, fats etc) of human tissue. On the other hand, a number of the elements are located in the extracellular fluids and can be accounted for in the liquid and solid waste fraction of humans. These elements fall into three major categories - cationic macroelements (e.g. Ca, K, Na, Mg and Si), anionic macroelements (e.g. P, S and Cl and17 essential microelements, (e.g. Fe, Mn, Cr, Co, Cu, Zn, Se and Sr). When provided in the recommended concentrations to an adult healthy human, these elements should not normally accumulate in humans and will eventually be excreted in the different human wastes.

A suite of cockpit navigation displays for low-visibility airport surface operations has been designed by researchers at NASA Ames Research Center following a human-centered process. This paper reports on the final research effort in this process that examined the procedural integration of these technologies into the flight deck. Using NASA Ames' high-fidelity Advanced Concepts Flight Simulator, eighteen airline crews completed fourteen low-visibility (RVR 1000′) land-and-taxi scenarios that included both nominal (i.e., hold short of intersections, route amendments) and off-nominal taxi scenarios designed to assess how pilots integrate these technologies into their procedures and operations. Recommendations for integrating datalink and cockpit displays into current and future surface operations are provided.

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.