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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.

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.

How much integrated system level test should be performed on a spacecraft before it is launched? Although sometimes system test is minimized, experience shows that systems level testing should be thorough and complete. Reducing subsystem testing is a less dangerous way to save cost, since it risks finding problems later in system test, while cutting systems test risks finding them even later on orbit. Human-rated spacecraft test planning is informal, subjective, and inconsistent, and its extent is often determined by the decision maker's risk tolerance, decision-making style, and long-term or short-term view. Decisions on what to test should be guided by an overall mission cost-benefit analysis, similar to the risk analysis used to guide development efforts.

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.

The Centrifuge Accommodation Module (CAM) will be the home of the fundamental biology research facilities on the International Space Station (ISS). These facilities are being built by the Biological Research Project (BRP), whose goal is to oversee development of a wide variety of habitats and host systems to support life sciences research on the ISS. The habitats and host systems are designed to provide life support for a variety of specimens including cells, bacteria, yeast, plants, fish, rodents, eggs (e.g., quail), and insects. Each habitat contains specimen chambers that allow for easy manipulation of specimens and alteration of sample numbers. All habitats are capable of sustaining life support for 90 days and have automated as well as full telescience capabilities for sending habitat parameters data to investigator homesite laboratories.

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.

This paper considers the design of a life support system for transit to Mars and return to Earth. Because of the extremely high cost of launching mass to Mars, the Mars transit life support system must minimize the amount of oxygen, water, and food transported. The three basic ways to provide life support are to directly supply all oxygen and water, or to recycle them using physicochemical equipment, or to produce them incidentally while growing food using crop plants. Comparing the costs of these three approaches shows that physicochemical recycling of oxygen and water is least costly for a Mars transit mission. The long mission duration also requires that the Mars transit life support system have high reliability and maintainability. Mars transit life support cannot make use of planetary resources or gravity. It should be tested in space on the International Space Station (ISS).

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 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 Columbia Accident Investigation Board issued a major recommendation to NASA. Prior to return to flight, NASA should develop and implement a comprehensive inspection plan to determine the structural integrity of all Reinforced Carbon-Carbon (RCC) system components. This inspection plan should take advantage of advanced non-destructive inspection technology. This paper describes a non-intrusive technology with a micro-flying robot to continuously monitor inside a space vehicle for any stress related fissures, cracks and foreign material embedded in walls, tubes etc.

Spacelab Life Sciences-3 (SLS-3) was scheduled to be the first United States man-tended microgravity flight containing Rhesus monkeys. The goal of this flight as in the five untended Russian COSMOS Bion flights and an earlier American Biosatellite flight, was to understand the biomedical and biological effects of a microgravity environment using the non-human primate as human surrogate. The SLS-3/Rhesus Project and COSMOS Primate-BIOS flights all utilized the rhesus monkey, Macaca mulatta. The ultimate objective of all flights with an animal surrogate has been to evaluate and understand biological mechanisms at both the system and cellular level, thus enabling rational effective countermeasures for future long duration human activity under microgravity conditions and enabling technical application to correction of common human physiological problems within earth's gravity, e.g., muscle strength and reloading, osteoporosis, immune deficiency diseases.

Accurate root zone moisture control in microgravity plant growth systems is problematic. With gravity, excess water drains along a vertical gradient, and water recovery is easily accomplished. In microgravity, the distribution of water is less predictable and can easily lead to flooding, as well as anoxia. Microgravity water delivery systems range from solidified agar, water-saturated foams, soils and hydroponics soil surrogates including matrix-free porous tube delivery systems. Surface tension and wetting along the root substrate provides the means for adequate and uniform water distribution. Reliable active soil moisture sensors for an automated microgravity water delivery system currently do not exist. Surrogate parameters such as water delivery pressure have been less successful.

A processing system has been developed to meet increasing demands for detailed noise measurement of aircraft in wind tunnels. Phased arrays enable spatial and amplitude measurements of acoustic sources, including low signal-to-noise sources not measurable by conventional measurement techniques. The Microphone Array Phased Processing System (MAPPS) provides processing and visualization of acoustic array measurements made in wind tunnels. The system uses networked parallel computers to provide noise maps at selected frequencies in a near real-time testing environment. The system has been successfully used in two subsonic, hard-walled wind tunnels, the NASA Ames 7- by 10-Foot Wind Tunnel and the NASA Ames 12-Foot Wind Tunnel. Low level airframe noise that can not be measured with traditional techniques was measured in both tests.

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.

The Research Animal Holding Facility (RAHF) is a spaceflight-qualified hardware system for housing adult rats. The Neurolab Space Shuttle mission, targeted for February 1998 on STS-89, will include neuroscience experiments involving nursing rat dams and neonates (newborn rats). Rat neonates have never been previously flown for spaceflight experimentation, and they present unique life support, science, and engineering challenges in the Spacelab microgravity environment. Modifications of the RAHF (with an associated comprehensive testing program, including spaceflight) are currently underway at NASA Ames Research Center (ARC), in order to add to the RAHF the capability of supporting nursing dams and neonates in preparation for Neurolab.

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.

Within NASA's Aviation Safety Program, the Aviation System Monitoring and Modeling (ASMM) Project addresses the need to provide decision makers with the tools to identify and evaluate predisposing conditions that could lead to accidents. This Project is developing a set of automated tools to facilitate efficient, comprehensive, and accurate analyses of data collected in large, heterogeneous databases throughout the National Aviation System. This report is a brief overview of the ASMM Project as an introduction to the rest of the presentations in this session on one of its key elements---the Performance Data Analysis and Reporting System (PDARS).

The On-line Project Information System (OPIS) is a LAMP-based (Linux, Apache, MySQL, PHP) system being developed at NASA Ames Research Center to improve Agency information transfer and data availability, largely for improvement of system analysis and engineering. The tool will enable users to investigate NASA technology development efforts, connect with experts, and access technology development data. OPIS is currently being developed for NASA's Exploration Life Support (ELS) Project. Within OPIS, NASA ELS Managers assign projects to Principal Investigators (PI), track responsible individuals and institutions, and designate reporting assignments. Each PI populates a “Project Page” with a project overview, team member information, files, citations, and images. PI's may also delegate on-line report viewing and editing privileges to specific team members. Users can browse or search for project and member information.