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The community of North Birmingham, Alabama, participated in the Communities LEAP pilot program. As part of this technical assistance pilot, the Alabama Clean Fuels Coalition conducted community engagement in the community to learn about the state of transportation and community priorities. This report highlights results and provides opportunities for improvements.
Some of the effects of horizontal visual restriction on the front cockpit of a T-33 aircraft were studied. These studies are pertinent to the establishment of guidelines that will be used in canopy design for limited visibility situations. Results of the study revealed that runway extension lines are helpful for restricted visibility situations. The superiority of a 300-foot runway over a 200-foot runway was greater than expected from geometric considerations. It was also shown that practice learning has a noticeable effect on performance. Finally, visibility restrictions that force a pilot into shallow glides should be avoided, and the available visibility should be sufficient to provide adequate information so that the pilot can solve the lateral-directional and pitch tasks simultaneously.
During the spring of 2016, from April 4 - May 27, sixteen GLOBE schools participated in the GLOBE Aerosol Field Campaign - U.S. Pilot Study. Thirteen teachers from these schools had previously participated in the NASA LEARN program (Long-term Experience in Authentic Research with NASA) where they were GLOBE trained in Atmosphere protocols, and engaged in 1-3 years of research under the mentorship of NASA scientists. Each school was loaned two aerosol instruments for the Campaign duration, either 2 GLOBE sun photometers, 2 Calitoo sun photometers, or 1 of each. This allowed for students to make measurements side-by-side and in the case of the Calitoos, to compare AOT results immediately with each other for better consistency in data collection. Additionally, as part of the Field Campaign evaluation, multiple instruments allow for an assessment of the ease of use of each instrument for grade level of students, whether in middle school or high school. Before the Campaign, all GLOBE and Calitoo instruments were 'checked out' against an AERONET, then checked again upon return after the Campaign. By examining all data, before, during and after the Campaign, this gives an indication of instrument performance and proficiency obtained by the students. Support was provided to each teacher and their students at the level requested, via email, phone or video conferencing.
The EMS flight environment is unique in today's aviation. The pilots must respond quickly to emergency events and often fly to landing zones where they have never been before . The time from initially receiving a call to being airborne can be as little as two to three minutes. Often the EMS pilot is the only aviation professional on site, they have no operations people or other pilots to aid them in making decisons. Further, since they are often flying to accident scenes, not airports, there is often complete weather and condition information. Therefore, the initial decision that the pilot must make, accepting or declining a flight, can become very difficult. The accident rate of EMS helicopters has been relatively high over the past years. NASA-Ames research center has taken several steps in an attempt to aid EMS pilots in their decision making and situational awareness. A preflight risk assessment system (SAFE) was developed to aid pilots in their decision making, and was tested at an EMS service. The resutls of the study were promising and a second version incorporating the lessons learned is under development. A second line of research was the development of a low cost electronic chart display (ECD). This is a digital map display to help pilots maintain geographical orientation. Another thrust was undertaken in conjunction with the Aviation Safety Reporting System (ASRS). This involved publicizing the ASRS to EMS pilots and personnel, and calling each of the reporters back to gather additional information. This paper will discuss these efforts and how they may positively impact the safety of EMS operations.
The Dropping In a Microgravity Environment or DIME competition for high-school-aged student teams has completed the first year for nationwide eligibility after two regional pilot years. With the expanded geographic participation and increased complexity of experiments, new lessons were learned by the DIME staff. A team participating in DIME will research the field of microgravity, develop a hypothesis, and prepare a proposal for an experiment to be conducted in a NASA microgravity drop tower. A team of NASA scientists and engineers will select the top proposals and then the selected teams will design and build their experiment apparatus. When completed, team representatives will visit NASA Glenn in Cleveland, Ohio to operate their experiment in the 2.2 Second Drop Tower and participate in workshops and center tours. NASA participates in a wide variety of educational activities including competitive events. There are competitive events sponsored by NASA (e.g. NASA Student Involvement Program) and student teams mentored by NASA centers (e.g. For Inspiration and Recognition of Science and Technology Robotics Competition). This participation by NASA in these public forums serves to bring the excitement of aerospace science to students and educators.Researchers from academic institutions, NASA, and industry utilize the 2.2 Second Drop Tower at NASA Glenn Research Center in Cleveland, Ohio for microgravity research. The researcher may be able to complete the suite of experiments in the drop tower but many experiments are precursor experiments for spaceflight experiments. The short turnaround time for an experiment's operations (45 minutes) and ready access to experiment carriers makes the facility amenable for use in a student program. The pilot year for DIME was conducted during the 2000-2001 school year with invitations sent out to Ohio- based schools and organizations. A second pilot year was conducted during the 2001-2002 school year for teams in the six-state region of Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. The third year for DIME was conducted during the 2002-2003 school year for teams from the fifty United States, the District of Columbia, and Puerto Rico. An annual national DIME program is planned for the foreseeable future. Presented in this paper will be a description of DIME, an overview of the planning and execution of such a program, results from the first three years, and lessons learned from the first national competition.
The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.
The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.
The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.
In fall 2020, the Colorado Energy Office, as part of the State of Colorado's "Can Do Colorado" initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, start in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The non-motorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO2. Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.
Development of computer aided formats, results of computer aided testing, learning in computer aided testing of critical in-flight event (CIFE) diagnosis and computer aided prompting are considered.
A number of rotary wing flight tests have been conducted by the Army Aeroflightdynamics Directorate in cooperation with NASA which involved the use of night vision devices and simulated devices. The test set up and data analysis have been taken from two perspectives. Some of the flight tests were structured to look at aircraft handling qualities when the pilot's image quality was reduced from normal daylight levels. In this case, aircraft flight path information was given to the pilot as one input into the Handling Qualities Ratings. Other flight tests were structured to look at pilot and workload directly. In this second case, aircraft position was accurately measured and used as pilot performance data. This paper provides an overview of the test methods used, lessons learned, and recommendations for future tam of night vision devices.
Developing and refining methods for teaching systems engineering is part of Systems Engineering grand challenges and agenda for research in the SE research community. Retention of systems engineering knowledge is a growing concern in the United States as the baby boom generation continues to retire and the faster pace of technology development does not allow for younger generations to gain experiential knowledge through years of practice. Government agencies, including the National Aeronautics and Space Administration (NASA), develop their own curricula and SE leadership development programs to "grow their own" systems engineers. Marshall Space Flight Center (MSFC) conducts its own Center-focused Marshall Systems Engineering Leadership Development Program (MSELDP), a competitive program consisting of coursework, a guest lecture series, and a rotational assignment into an unfamiliar organization engaged in systems engineering. Independently, MSFC developed two courses to address knowledge retention and sharing concerns: Real World Marshall Mission Success course and its Case Study Writers Workshop and Writers Experience. Teaching case study writing and leading students through a hands-on experience at writing a case study on an SE topic can enhance SE training and has the potential to accelerate the transfer of experiential knowledge. This paper is an overview of the pilot experiences with teaching case study writing, its application in case study-based learning, and identifies potential areas of research and application for case study writing in systems engineering education.
NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.
NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.
Data are presented that show the effects of visual and motion during cueing on pilot performance during takeoffs with engine failures. Four groups of USAF pilots flew a simulated KC-135 using four different cueing systems. The most basic of these systems was of the instrument-only type. Visual scene simulation and/or motion simulation was added to produce the other systems. Learning curves, mean performance, and subjective data are examined. The results show that the addition of visual cueing results in significant improvement in pilot performance, but the combined use of visual and motion cueing results in far better performance.
The training of pilots at major US air carriers has changed little over the years. These courses often begin in learning each and every sub-system of the particular airplane to be flown. The training course culminates in a series of training sessions in a full-motion, full-mission flight simulator. Most US airlines now incorporate one final simulator training session known as LOFT, Line Oriented Flight Training, where rather than going through a series of disconnected, isolated, flight maneuvers, the session is conducted as a flight from a departure airport to a destination airport. Flying for an Air Line, one is a Line Pilot, flying the "line." And so the LOFT session in the simulator is supposed to represent the reality of line operations and thus prepare the trainee to fly the line. But if the ultimate goal of the training is to produce a pilot who is ready to safely and efficiently fly the line, shouldn't all training be "line oriented"? This paper describes an approach to structuring airline flight training such that all training is "line oriented." This approach represents an opportunity to translate many of the training principles offered by Healy and her colleagues into training specifications.
Urban heat islands increase the demand for cooling energy and accelerate the formation of smog. They are created when natural vegetation is replaced by heat-absorbing surfaces such as building roofs and walls, parking lots, and streets. Through the implementation of measures designed to mitigate the urban heat island, communities can decrease their demand for energy and effectively "cool" the metropolitan landscape. Measures to reverse the urban heat island include afforestation and the widespread use of highly reflective surfaces. To demonstrate the potential benefits of implementing these measures, EPA has teamed up with NASA and LBNL to initiate a pilot project with three U.S. cities. As part of the pilot, NASA is using remotely-sensed data to quantify surface temperature, albedo, the thermal response number and NDVI vegetation of each city. To pursue these efforts, more information is needed about specific characteristics of several different cities. NASA used the Advanced Thermal and Land Applications Sensor (ATLAS) to obtain high spatial resolution (10 m pixel resolution) over each of the three pilot cities (Baton Rouge, Sacramento, and Salt Lake City). The goal of the UHIPP is to use the results from the NASA/LBNL analysis, combined with knowledge gained through working with various organizations within each pilot city to identify the most effective means of implementing strategies designed to mitigate the urban heat island, These "lessons learned" will be made available and used by cities across the U.S. to assist policy makers and others within various communities to analyze their own urban heat islands and determine which, if any, measures can be taken to help save energy and money, and to prevent pollution. The object of this session is for representatives from each of the pilot cities to present their results of the study and share the experience of working with these data in managing their urban landscape.
This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.