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NASA NTRS2016

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

anomaly detection↗

NASA NTRS2017

Characterization of Metering, Merging and Spacing Requirements for Future Trajectory-Based Operations

Trajectory-Based Operations (TBO) is one of the essential paradigm shifts in the NextGen transformation of the National Airspace System. Under TBO, aircraft are managed by 4-dimensional trajectories, and airborne and ground-based metering, merging, and spacing operations are key to managing those trajectories. This paper presents the results of a study of potential metering, merging, and spacing operations within a future TBO environment. A number of operational scenarios for tactical and strategic uses of metering, merging, and spacing are described, and interdependencies between concurrent tactical and strategic operations are identified.

Johnson, Sally↗

NASA NTRS2017

Viscous Three-Dimensional Simulation of Flow in an Axial Low Pressure Compressor at Engine Icing Operating Points

Viscous three-dimensional simulations of the Honeywell ALF502R-5 low pressure compressor (sometimes called a booster) using the NASA Glenn code GlennHT have been carried out. A total of ten simulations were produced. Five operating points are investigated, with each point run with two different wall thermal conditions. These operating points are at, or near, points where engine icing has been determined to be likely. In the future, the results of this study will be used for further analysis such as predicting collection efficiency of ice particles and ice growth rates at various locations in the compressor. A mixing plane boundary condition is used between each blade row, resulting in convergence to steady state within each blade row. The k-omega turbulence model of Wilcox, combined with viscous grid spacing near the wall on the order of one, is used to resolve the turbulent boundary layers. For each of the operating points, heat transfer coefficients are generated on the blades and walls. The heat transfer coefficients are produced by running the operating point with two different wall thermal conditions and then solving simultaneously for the heat transfer coefficient and adiabatic wall temperature at each point. Average Nusselt numbers are calculated for the most relevant surfaces. The values are seen to scale with Reynolds number to approximately a power of 0.7. Additionally, images of surface distribution of Nusselt number are presented. Qualitative comparison between the five operating points show that there is relatively little change in the character of the distribution. The dominant observed effect is that of an overall scaling, which is expected due to Reynolds number differences. One interesting aspect about the Nusselt number distribution is observed on the casing (outer diameter) downstream of the exit guide vanes (EGVs). The Nusselt number is relatively high between the pairs of EGVs, with two lower troughs downstream of each EGV trailing edge. This is of particular interest since rather complex ice shapes have been observed in that region.

compressor↗

NASA NTRS2017

Key Differences in Operating a Rover on the Moon vs. Mars

The command and control model for spacecraft operations, as well as the distribution of tasks between ground assets and in space assets, whether with a crew or solely robotic, is fundamentally constrained by the round trip light time between the space asset and the control facility (presumably on Earth, though not required). For an asset on Mars, the round trip light time varies, from roughly fourteen minutes to up to forty minutes. For a Lunar asset the round-trip light time is measured in only a few seconds, but current communications systems may more than double the latency with system overhead. For a Lunar Asset the total command latency may range from six seconds to more than forty, depending on communications overhead and data rates. Further, these variables are not always predictable, thus complicating operations. There are several differentiating factors for Lunar vs. Mars operations, Round trip light time/Atmosphere/Lighting and ShadowsTerrain type and knowledge/Round trip light time has implications for the distribution of tasks between ground and in space assets. Even at Lunar Distances, the combination of round trip light time plus communications systems overhead does not enable joy stick driving of a rover. The best that can be done, if driving from Earth, is near real time command and control. By 2030, driving from in space may be possible. Productivity on Mars requires either long operational sequences of commands, as is done for current rovers such as Curiosity, significant autonomous capability or, as may be possible by 2030, command and control support from space. Another implication of the long round trip light time from Earth to Mars, is that flight software functions must be resident on the in space asset. On the Moon, there is considerably more flexibility, enabling processing functions, to be resident on Earth or in space. This provides the opportunity to take advantage of the considerable processing power available on the ground, but may be constrained by data rates. On the Moon, for practical operational purposes, there is no atmosphere. Hence there is no scattering of light in the shadows. This has implications for image interpretation and driving near the poles. The Moon has permanently shadowed regions (PSR), unique terrain with unknown surface properties. With no scattering of light in shadows, driving on the Moon, particularly at the poles, where we have strong evidence of water, may prove to be hazardous and complex, requiring non-optical sensors, such as LIDAR.

Trimble, Jay↗

NASA NTRS2017

Concept of Operations for Integrating Commercial Supersonic Transport Aircraft into the National Airspace System

Several businesses and government agencies, including the National Aeronautics and Space Administration are currently working on solving key technological barriers that must be overcome in order to realize the vision of low-boom supersonic flights conducted over land. However, once these challenges are met, the manner in which this class of aircraft is integrated in the National Airspace System may become a potential constraint due to the significant environmental, efficiency, and economic repercussions that their integration may cause. This document was developed to create a path for research and development that exposes the benefits and barriers of seamlessly integrating a class of CSTs into the NAS, while also serving as a Concept of Operations (ConOps) which posits a mid- to far-term solution (2025-2035) concept for best integrating CST into the NAS. Background research regarding historic supersonic operations in the National Airspace System, assumptions about design aspects and equipage of commercial supersonic transport (CST) aircraft, assumptions concerning the operational environment are described in this document. Results of a simulation experiment to investigate the interactions between CST aircraft and modern-day air traffic are disseminated and are used to generate scenarios for CST operations. Finally, technology needs to realize these operational scenarios are discussed.

Underwood, Matthew C.↗

NASA NTRS2018

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

aviation safet↗

NASA NTRS2018

Astrobee: A New Tool for ISS Operations

Astrobee is a new class of free-flying robots that operate in the interior of the International Space Station (ISS). In addition to providing a research platform for zero-g free-flying robotics (replacing the Synchronized Position Hold Engage and Reorient Experimental Satellites (SPHERES)), Astrobee improves the efficiency of ISS operations by providing flight and payload controllers with a mobile camera/sensor platform. The Astrobee system consists of three robots, a docking station, and a ground data system. Development began in late 2014, and Astrobee will launch to ISS in late 2018. This paper provides an overview of the capabilities of the Astrobee system, the concept of operations for ISS operations support, and the challenges of operating a free-flying robot within the constraints of the ISS environment.

Astrobee↗

NASA NTRS2018

Aseptic Operations for Post DHMR Processing of MOMA Mass Spectrometer

Mars Organic Molecule Analyzer - Mass Spectrometer (MOMA-MS) is an instrument in the MOMA instrument suite for the European Space Agency (ESA) ExoMars 2020 Rover. The rover is Planetary Protection Mission Category IVb, the first IVb mission since the Viking missions in the 1970s. Within the sample path of the MOMA instrument suite, the hardware surfaces of the must be sanitized to a level of less than 0.03 spore/m sq. To meet this requirement, the MS sample path was subjected to Dry Heat Microbial Reduction (DHMR) to decrease the number of viable spores by 4 orders of magnitude from a measured 88 spores/m sq to 0.009 spores/m sq. Before DHMR, the hardware is handled using standard cleanroom practices. After DHMR, planetary protection filters protect the sample path for most of integration, but when sample path exposure is required, aseptic operations are instituted and exposure times are kept to an absolute minimum. The surface area of exposure is also taken into account to determine safe exposure times. Before work begins, the ISO class 5 aseptic workspace is cleaned and tested for surface and airborne bioburden, and all tools that will contact or be used near sample path surfaces are sterilized. During the exposure activity, sterile garments are worn, sterile gloves are changed as often as necessary, and the environment is monitored with active and passive fallout for bioburden and real time airborne particle counts. Sterile tools are handled by a two person team so that the operator touches only the tool and not the exterior surfaces of the sterilization pouch, and a sterile operating field is established as a safe place to organize tools or parts during the aseptic operations. In cases where aseptic operations are not feasible, localized DHMR is used after exposure. Any breach in the planetary protection cleanliness can necessitate repeating instrument level DHMR, which not only has significant cost and schedule implications, it also become a risk to hardware that is not rated for repeated long exposures to high temperatures.

Spacecraft Design↗

NASA NTRS2018

Hybridized Agile Software Development of Flight Control Team Tools for International Space Station's Payload Operations Integration Center

Ground systems operations at the National Aeronautics and Space Administration's (NASA) Payload Operations and Integration Function (POIF) is increasing, via the High Operations Tempo (HOT) initiative, its ability to support more science activities with a fourth crew member on the International Space Station (ISS). The Flight Control Team's need to support this increased crew presence mandated the need for a series of software tools to better handle the increasing pace of payload science operations. The overall need was clear from the outset, but the full scope and user experience for each tool, were not as well understood, so establishing a fixed set of initial requirements was not feasible. An Agile Software Development (ASD) paradigm takes advantage of uncertainty, and plans for it, so it was deemed the most appropriate approach to create room for exploring novel concepts, and to mount a rapid and flexible response to inevitiably changing requirements. It facilitated the need for unprecedented collaboration between the Product Team (i.e. users from the Flight Control Team) and the Development Team (i.e. POIC systems engineers, developers, testers). This is a process shift in the development, test, and release of software from one that is prescriptive to one that is adaptive, which is necessary for these tools to have longevity. The application of ASD to the product development lifecycle permitted the timely incorporation of customer feedback, and, allowed for continuous quality improvements. This resulted in a suite of tools that are efficient, user-friendly, and enable POIF ground systems to support the increasing pace of payload science operations. ASD is not as much a set of prescriptive processes as it is a shift in mindset; one that moves from planning against change, to planning for change, thereby iteratively growing software towards user-defined value.

Albers, Cerese M.↗

NASA NTRS2018

Astrobee: A New Tool for ISS Operations

ISS↗

NASA NTRS2018

Methods to Reduce Communication Workload for UAM Operations

Implementation of Urban Air Mobility (UAM) operations, or air passenger transportation systems within densely populated metropolitan areas, seeks to mitigate increasing traffic congestion. However, the development and integration of UAM operations into the national airspace system comes with its own unique challenges, such as vehicle requirements, flight planning and scheduling, and coordination between UAM flights and air traffic controllers. In particular, verbal coordination will play an integral part in the determined success of UAM operations and its ability to meet projected high consumer demands. In order to meet demands and higher traffic volumes on UAM routes, verbal communication between the UAM pilot and controller must be streamlined to reduce the controller's workload while helping to maintain safety within a given airspace. One method of reducing verbal workload are Letters Of Agreement (LOAs) that outline responsibilities and procedures for operations in an airspace. These LOAs will specify the operations, procedures, and routes for UAM flights. The proposed study will examine the usability of two route formatting styles for LOAs; (i) Verbal route descriptions and (ii) Tower En Route Control (TECs) routes. Verbal route descriptions will include the route name and associated visual cues on the route. The TEC route versions will include relevant waypoints and charts outlining the route with waypoints marked. The study will be part of a UAM X1 human in the loop (HITL) simulation. Controller participants will handle traditional air traffic including moderate levels of UAM traffic on current and modified helicopter routes within the Dallas Fort-Worth area. Scenarios will be counterbalanced and repeated to test both route formatting versions. After each trial, participants will rate the usability of the LOA used in the previous trial via a subjective questionnaire. We expect that controllers will prefer the LOA with TEC routes due to simplicity and visual elements available.

aerospace human factors↗

NASA NTRS2017

Flight Deck Surface Trajectory-Based Operations (STBO): A Four-Dimensional Trajectory (4DT) Simulation

Within human factors there is burgeoning interest in the Human-Autonomy Teaming (HAT) concept as away to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous automation as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as plays (Shively et al., 2017). It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations (Nourbakhsh et al., 2005), healthcare and medicine (Tsui Yanco, 2007), autonomous vehicles (Parasuraman, Barnes, Cosenzo, Mulgund, 2007), photography (Lachter, Brandt, Sadler, Shively, in press), and aviation (Shively et al., in press). The current paper presents one such effort to apply HAT. It details the design of a R-HAT Agent developed as part of a NASA Research Agreement awarded to Human-Autonomy Teaming Solutions Inc. (HATS Inc), and developed in collaboration with the Human-Autonomy Teaming Laboratory at NASA Ames Research Center. The role of this Agent is to mediate interaction between the automation and the human operator of an advanced ground dispatch station, with this mediation based upon previously mentioned core tenets for HAT and the many lessons learned from the HAT research literature. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO; Lachter, Brandt, Battiste, Matessa, Johnson, in press). Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by the Agent, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, the Agent determines what tasks need to be done and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. The R-HAT Agent has been designed to both support end users and research in RCO and HAT. Additionally, the Agent and its underlying architecture were developed with generalizability in mind as a modular piece of software applicable outside of RCO aviation in domains such as those mentioned above. This paper will also discuss future further development and testing of the R-HAT Agent.

Bakowski, Deborah L.↗

NASA NTRS2016

Characterization of Operational Performance of Ka-Band Links in Deep Space Network

This paper presents a characterization of performance of 32-GHz Ka-band link in an operational environment. The data come from tracking of Kepler spacecraft by the NASA Deep Space Network (DSN) in the past few years. Ka-band link offers a significant signal-to-noise advantage compared to the more common X- or S-band; however, it is subject to more signal fluctuation caused by the weather. Kepler is the first mission supported by the DSN that solely rely on the higher deep space Ka-band communications link to return its high-rate science data. A well characterization of the operational performance of Kepler would benefit future Ka-band missions, especially for those operating with smaller link margin. The study examines how weather conditions at the DSN facilities (e.g., winds, clouds, rains) affect the received signal, particularly on telemetry data. It addresses questions such as how often the weather affects the link and how much degradation the link could suffer. Among the 22 Ka-band passes in 2012, heavy clouds affected one pass and two passes were impacted by high winds. The adverse weather caused at time as much as 3-dB instantaneous change in the signal to noise ratio (SNR). Estimates of degradation to the SNR as a function of wind speeds are captured based on observations. This study also quantifies the probability distribution of the variation of received signal power. With such information, future missions can better plan their link design. An optimal link design aims for just having enough margin to realize the data return with a targeted probability, with neither having too much margin that it reduces the downlink data rate nor insufficient reserve that causes frequent data outages. The study tries to provide some answers to the questions such as how much does SNR vary from one tracking pass to the next, and what is the cumulative distribution of signal fluctuation. Regarding the signal variation over many tracking passes, we found that the averaged symbol SNR (SSNR) for each pass changed by as much as 4 dB. Some variations were due to geometry such as the changing distance between spacecraft and Earth and different antenna pointing elevations. Other variations seem to be random in nature, reflecting the randomness of operational environments. Some passes were found to be quite stable, with a standard deviation of symbol SNR around 0.25 dB while others had greater variation, up to 1.4 dB. Overall, the cumulative distribution of the symbol SNR reflects that 50% of the fluctuations was less than 0.6 dB, 90% of fluctuation was within 1.6 dB and 95% within 2.2 dB. Some of the challenges in data processing, validation and modeling were captured in this paper. We observed some inconsistent measurements where operational data significantly deviate from a normal expectation. These inconsistent measurements made it hard to develop an accurate and consistent performance model, such as the degradation of signal SNR as a function of high wind. The difficulty was compounded by the fact that there were only a few Ka-band passes observed in high winds.

Pham, Timothy↗

NASA NTRS2019

Operations of the Optical Communications Demonstration for the Orion EM-2 Mission

The GSFC implementation of an Optical Communication System to demonstrate an operational optical communication link for Orion EM-2. It will serve as a base for providing an operational optical communications capability for future Orion missions. GSFC plans to maintain a development path for the optical communication flight terminal to allow commercialization and implementation on future Orion missions. The Orion optical module part of the optical communications flight terminal and its control electronics have a common architecture with the ILLUMA-T optical terminal provided by GSFC for use on the ISS. The plan is to flow data from Orion through the Optical Communication Fight Terminal to the Optical Communication Ground Terminal and reverse. NASA's Orion spacecraft is an exploratory vehicle designed for longer-duration flights beyond the Moon. Following Orion's Exploration Mission-1 (EM-1), during which the spacecraft will travel beyond the Moon, enter a distant retrograde orbit around the Moon and return to Earth unmanned, Exploration Mission-2 (EM-2) will see a crewed spacecraft complete a slightly different flight path. First crewed test flight of the Orion spacecraft, currently targeting a June 30, 2022 launch. The mission involves: One revolution in Low Earth Orbit (LEO) parking orbit to verify basic Orion systems functionality and deploy solar arrays. A single 42-hour Highly Elliptical Orbit (HEO) intermediate checkout orbit allows characterization of the Orion vehicle system performance prior to committing to a cis-lunar flight. Trans Lunar Injection (TLI) burn using Orion Service Module (SM) main engine, which sends Orion on a lunar flyby and free return. Skip reentry at lunar return velocities to splashdown off the coast of San Diego. Total mission duration is approximately 10 days. EM-2 is the first crewed mission of the Orion Spacecraft that crew brings more video up/downloads, file transfers, and real-time chats with family back home and high-rate communications enables live HD streaming for Crew conferences, Public Affairs Office (PAO) events, and significant mission events. Also still collecting vehicle data on numerous Orion subsystems via Development Flight Instrumentation (DFI) allows large data volume returns sooner for DFI and future science payloads, as opposed to waiting for end-of-mission. O2O is a demonstration of operational utility system tested as a Developmental Test Objective (DTO) and not required to meet mission requirements/success/ Flight Test Objectives (FTO). EM-2 architecture is as close to future mission operational architecture as possible.Orion subsystems (video, DFI, etc.) expected to generate ~250 GB of data in the first 24 hours of flight. Total data generated over the mission estimated to be more than 400 GBUsing S-Band alone, Orion limited to ~ 6GB of data downlink per day. Because of this limitation, Orion is planning to limit live video downlinks on EM-1 in order to downlink high priority fileswith 1 hour/day of Optical Communication, Orion could downlink ~6x more data per day (~ 36GB/day).The optical communication system is capable of multiple data rates up to at least 80 Mbps downlink for the transfer of Orion data to Earth while Orion is operating in the lunar vicinity.

Optical Communication↗

NASA NTRS2019

NASA SEXTANT Mission Operations Architecture

The Station Explorer for X-Ray Timing and Navigation (SEXTANT) mission is a technology demonstration enhancement to the Neutron Star Interior Composition Explorer (NICER) mission, a NASA Astrophysics Explorer Mission of Opportunity to the International Space Station (ISS) that was launched in June of 2017. The NICER instrument is a precision pointing X-ray telescope that times pulsar sourced photon arrivals which the SEXTANT mission uses to perform autonomous onboard X-ray Pulsar Navigation (XNAV). By comparing the detected time of arrival of X-ray photons to a reference of expected pulsar timing models, one can infer a range and range rate measurement based on light time delay. Since both timing and orientation information comes from a celestial source, this technology could provide a GPS-like navigation capability available throughout our Solar System and beyond. Applications that XNAV can support include outer planet and interstellar missions, manned missions, libration orbit missions, and current infrastructure such as the Deep Space Network (DSN). The SEXTANT team successfully completed a first demonstration of in-space and autonomous XNAV in November 2017. NICER and SEXTANT have separate teams, with NICER being the primary team with its own science objectives. Operational modes for both missions must have concurrent and independent components as well as an integrated ground system. This paper describes on the operational infrastructure and successful operations for performing X-ray Pulsar Navigation with the Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) demonstration. This paper first details the infrastructure implemented, then the concept of operations, and finally the operations for the SEXTANT demonstration and lessons learned.

Ground Systems↗

NASA NTRS2016

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations.

Sridhar, Banavar↗

NASA NTRS2020

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the ‘in-time’ aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance.

Young, Steven↗

NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: NASA Case Studies

There is an increasing interest in applying methods based on Machine Learning Techniques(MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues relating to data, feature selection and validation of the models are illustrated by examining case studies of the application of MLT to problems in air traffic management at NASA. Further research is needed in the application of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗