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This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.

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.

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.

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.

Three-component Particle Image Velocimetry (3D PIV) is a fluid velocity measurement technique that has evolved from the laboratory to become a method appropriate for use in large-scale wind tunnel testing. An example application of 3D PIV in a wind tunnel test is described. The PIV technique was applied to characterize the wake of The Ground Transportation System (GTS) model developed for the Department of Energy (DOE) Heavy Vehicle Drag Reduction (HVDR) program. The test was performed in the Ames/Army 7×10 foot wind tunnel. The objective of the PIV measurements was to validate the HVDR computational fluid dynamics code. The PIV method and PIV system are described. Sample truck wake data with and without boattail attachments are shown. 3D PIV system successfully captured the effects of the boattails on the truck wake.

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.

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. Experimental 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 (USC). Companion computer simulations are being performed by Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL), and California Institute of Technology (Caltech) using state-of-the-art techniques.