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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.

This paper describes a multi-disciplinary damage detection methodology that can aid in detecting and diagnosing a damage in a given structural system, not limited to the example of a rotating gear presented here. Damage detection is performed on the gear stress data corresponding to the steady state conditions. The normal and damage data are generated by a finite-difference solution of elastodynamic equations of velocity and stress in generalized coordinates1. The elastodynamic solution provides a knowledge of the stress distribution over the gear such as locations of stress extrema, which in turn can lead to an optimal placement of appropriate sensors over the gear to detect a potential damage. The damage detection is performed by a multi-function optimization that incorporates Tikhonov kernel regularization reinforced by an added Laplacian regularization term as used in semi-supervised machine learning. Damage is mimicked by reducing the rigidity of one of the gear teeth.

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.