Results for “common references” — Page 16

NASA NTRSDate not supplied

Langley Automated Sensor Inter-calibration System (LASICS): Open Access Tools for Satellite Imager Inter-Calibration

Satellite imager calibration teams are tasked with maintaining stable measurement records to facilitate reliable monitoring of geophysical parameters and ensure dependable input for weather forecast models. Satellite imagers are neither uniformly calibrated nor radiometrically scaled to a common reference. Consistent inter-calibration between various earth-orbiting satellite imager pairs is a critical step in the creation of seamless earth-scene reflectance data records over time for input to higher level algorithms that retrieve earth system climate-sensitive properties. Each imager inherently by virtue of its optics (and associated properties like Spectral Response etc.) will have a unique measurement of the same earth-scene reflected signal. A key part of this is the identification and prediction of events where the imager pairs from their respective earth-orbits view the same stable terrestrial targets with nearly identical viewing and solar geometry. Langley Automated Sensor Inter-calibration System (LASICS) will provide the Earth remote sensing community with a foundation for the harmonization of these remote sensing records by the development of an intuitive, user-friendly, interactive web-based open access service that will leverage, an existing extensive and ever-growing database of earth-imager channel spectral response functions, on-demand capabilities for computation and visualization of spectral band adjustment factors using a variety of external hyper-spectral earth observation sources (e.g. SCIAMACHY, GOME-2 for VIS and IASI, AIRS for IR) and a variety of Solar Irradiance Spectra.

Arun Gopalan↗

NASA NTRSDate not supplied

Langley Automated Sensor Inter-calibration System (LASICS): Open Access Tools for Satellite Imager Inter- Calibration

Satellite imager calibration teams are tasked with maintaining stable measurement records to facilitate reliable monitoring of geophysical parameters and ensure dependable input for forecast models. Satellite imagers are neither uniformly calibrated nor radiometrically scaled to a common reference standard. Consistent inter-calibration between various earth-orbiting satellite imager pairs is a critical step in the creation of seamless earth-scene reflectance data records over time for input to higher level algorithms that retrieve earth system climate-sensitive properties. Each imager inherently by virtue of its optics (and associated properties like spectral response etc.) and orbit will have a unique measurement of the same earth-scene reflected signal. A key part of this is the computationally efficient and optimal identification and prediction of science opportunities where the imager pairs from the irrespective earth-orbits near-simultaneously view the same stable terrestrial targets with nearly identical viewing and solar geometry.

Arun Gopalan↗

NASA NTRS1993

(abstract) The Impactor Flux in the Pluto-Charon System

The impactor flux on Pluto and Charon consists of long-period comets from the Oort cloud passing through the region, and intermediate-period comets evolving inward to (or outward from) short-period orbits. The source of the intermediate-period comets is likely an extended disk of remnant planetesimals beyond the orbit of Neptune, commonly referred to as the Kuiper belt, though the Oort cloud may be an additional source of some short-period comets. The Pluto-Charon system actually circulates within the Kuiper belt, and with an aphelion of 49.3 AU, moves through a region where remnant planetesimal orbits would likely be stable over the age of the solar system. The recent discovery of two objects at Kuiper belt distances, 1992 QB 1 and 1993 FW, and the fact that the orbit determined for 1992 QB 1 is likely of low eccentricity and low inclination, have provided strong observational evidence for the Kuiper belt. Weisman et al (1989) showed that, for the expected number of objects in the Kuiper belt necessary to provide the short-period comet flux, cometary cratering on Pluto and Charon is dominated by Kuiper belt comets. We will provide updated estimates of the numbers, based on an improved understanding of Oort cloud and Kuiper belt dynamics since 1989, and on improved estimates for the radii and masses for Pluto and Charon. Because Pluto and Charon actually penetrate the stable region of the Kuiper belt beyond 40 AU, expected cratering rates for the pair are substantially higher than for Triton, which is located at the inner edge of the Kuiper belt. However, the Pluto-Charon system is tidally evolved and this may have led to resurfacing events at various times in its history. Thus, detailed predictions of the integrated crater density or comparisons with Triton are not very meaningful without a knowledge of that history. Differences in the suspected compositions of Pluto and Charon may also lead to different surface rheologies and thus, different retention times for cratered surfaces.

Pluto↗

NASA NTRSDate not supplied

A Conceptual Model for Integrating Design Thinking and Lean Startup Methods into the Innovation Process

Design thinking is a methodology that comes from the industrial design realm and is centred on culling better insight into needs from users. Another popular methodology is based on gaining insight on the potential of an opportunity through experimentation, testing, and iterating with users. These are commonly referred to as lean startup methods. However, from a research perspective, we still do not know the most effective way to implement these user-focused design methods within the innovation process within organizations, and which aspects of the design process are the most impactful in developing new opportunities. In this research, we propose a high-level conceptual process model on how user-focused design methods such as design thinking and lean startup methods can be integrated into the up-front innovation process within organizations. This is based on a mix of digital ethnography, business historiography, and ethnographic, embedded participant observation of an organization's development of an innovation process. We review the conceptual model, associated activities, and process considerations. The article concludes with thoughts on future research.

Design Innovation↗

NASA NTRSDate not supplied

EARLY INFORMATION PARAMETER-SET ANALYSIS FOR SATELLITE CLOSE APPROACHES USING MACHINE LEARNING

In spaceflight navigation applications, understanding and accurately applying orbital mechanics by leveraging force models for trajectory predictions will always remain an important aspect in space mission design and operations. In the process of capturing the dynamics and perturbations in the space environment, the force models are not all encompassing in that these models are subject to errors, commonly referred to as process noise. Therefore, in predicting state vectors of space objects such as spacecraft or debris over long periods of time, these errors in the process noise tend to grow over time.

machine learning↗

NASA NTRS2022

GPM Ground Validation at NASA Wallops Precipitation Research Facility

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, VA has been established as a semi-permanent super-site for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research quality precipitation instruments, including NASA’s S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual Frequency Precipitation Radar (DPR), combined DPR-GPM Microwave Imager (GMI) and GMI Level II precipitation retrievals compared to WPRF ground observations from a six-year collection of satellite overpasses. Multi-sensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (D(sub m)), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 minutes after overpass time but are not consistent between cases. In addition, GPM Core D(sub m) retrievals are within Level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (< 1.0 mm hr(exp -1)). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multi-sensor DPR/GMI combined algorithms outperform single sensor DPR algorithm.

Charanjit S Pabla↗

NASA NTRSDate not supplied

A Generalized Guidance Approach to In-Space Solid-Propellant Vehicle Maneuvers

Exploration-class vehicles that require fully autonomous ascent and descent must employ robust, explicit path-adaptive guidance algorithms that can operate in a wide range of physical environments. Vehicle designs that employ solid-propellant rocket motors (SRMs) for maneuvers are attractive from a systems engineering perspective because of their simplicity and reliability, but may cause complications for both mission designers and GNC engineers when dealing with total impulse uncertainty, as well as proper energy management of a motor with an uncontrolled cutoff time. This paper presents a simplified guidance algorithm, named Simple Cross-Product Steering (SxS), that was derived during early studies of NASA’s Mars Sample Return mission’s Mars Ascent Vehicle. The algorithm takes roots in a flight-proven guidance algorithm commonly referred to as Cross-Product Steering. SxS has been shown to provide sufficient guidance accuracy for in-space SRM burns in a simulated Martian environment, and preliminary studies have been conducted to test the algorithm in a solid-propellant lunar braking scenario. A method for predicting proper motor ignition time during execution of the Cross-Product Steering algorithm is the primary contribution of this paper. Mechanization notes are also provided that were realized in early phases of MAV. Results are shown for an example ascent vehicle in a simulated Mars environment.

GNC↗

NASA NTRSDate not supplied

A Comparison of NO Laser-Induced Fluorescence Models at Conditions Relevant to Supersonic and Hypersonic Flows

Planar laser-induced fluorescence (PLIF) of the nitric oxide (NO) molecule has been widely used in wind tunnel facilities for flow visualization, velocity, and temperature measurements. The experimental PLIF measurements are often compared with synthetic PLIF images using computationally derived temperatures, pressures, velocities, and species mole fractions. This approach is commonly referred to as computational flow imaging (CFI). In the present work, we compare signal intensity from PLIF models with experimental PLIF measurements obtained within a low pressure gas cell system at pressures and NO mole fractions relevant to supersonic and hypersonic flowfields. Experimental measurements were compared to several different laser induced-fluorescence models reported in the literature including LIFBASE, LINUS, and a NASA two-level model. The experimental measurements agreed well with all of the models at lower pressures and lower NO mole fractions; the fluorescence there is linear with both of these parameters. However, at higher pressures and mole fractions, the signal becomes nonlinear with respect to these parameters as self-quenching limits the signal and absorption further limits the signal. In fact, for the experimental path length of the experiment, the combination of high pressure and high NO mole fraction causes the experimental results to deviate significantly from the predicted results that neglect absorption of the incident laser sheet. The LINUS model, which allows absorption to be calculated, provided results that agreed better with the experimental measurements. Since supersonic and hypersonic flowfields may contain a region of the flow with high pressures and measurements in large-scale facilities often include a long path length, neglecting absorption may have a significantly negative effect on the CFI comparison to experimental PLIF images. As a result, PLIF models that account for absorption should be included in computational flow imaging approaches for laser induced fluorescence.

laser-induced fluorescence↗

NASA NTRS2022

A High-fidelity Performance and Sensitivity Analysis of X-ray Pulsar Navigation in Near-Earth and Cislunar Orbits

Millisecond X-ray pulsars are rapidly rotating neutron stars that emit broadband electromagnetic radiation, including X-rays. These emissions can appear as pulsations to distant observers with long-term stabilityrivaling laboratory atomic clocks. The concept of using X-ray pulsars for navigation, commonly referred to as XNAV, has a long history in the research literature. Notably, in 2017, NASA’s Station Explorer for X-ray Timing and Navigation (SEXTANT) mission,a technology enhancement to the Neutron Star Interior Composition Explorer, made a successful on-orbit demonstration of XNAV on the International Space Station (ISS) in Low-Earth-Orbit. This paper investigates the performance of XNAV in Earth and lunar regimes using a new high-fidelity XNAV analysis tool that builds on tools developed for SEXTANT and uses recent updates to the Goddard Enhanced Onboard Navigation System flight software. Specifically, we study the use of XNAV in an ISS-like orbit for comparison with SEXTANT results, in a highly-inclined geosynchronous orbit, and in an Earth-Moon L1 libration point Near Rectilinear Halo Orbit similar to that proposed for the planned lunar Gateway space station. This paper provides a high-level description of the XNAV analysis tool, baseline predicted performance results, selected sensitivity analyses, and conclusions.

Luke B Winternitz↗

NASA NTRS2022

Separating Propulsive Mass and Energy for Space Applications

Initially and traditionally, space access and in-space propulsion utilized combustion and expulsion of chemical “fuels” carried on board, with performance governed by the rocket equation. These chemicals produced energy, and after combustion, constituted the propulsive mass/momentum. The best such chemicals in terms of Isp that are deemed safe engineering/mission wise are hydrogen and oxygen, producing some 450 seconds of Isp. There are more reactive chemicals, such as fluorine, which have higher Isp, but also have serious safety issues. Propulsion in atmospheres can ingest atmospheric constituents (aka “airbreathing propulsion”) which provide additional propulsive mass when heated and a component of the combustion/energy generation process. This utilization of non-stored/carried propulsive mass provides partial separation of propulsive mass and energy and produces higher Isp. The other approach to separating propulsive mass and energy is to, either on board or added from offboard, supply additional energy, such as from nuclear or solar sources. This is the approach for fission nuclear thermal and electric propulsion, which can provide an Isp in excess of 800 seconds of Isp. Other processes or in addition to thermal expansion, such as electro-magnetics, can be employed to increase exit velocity and Isp. Separating propulsive mass and energy for space faring is commonly referred to as a means to circumvent the rocket equation and is capable of producing major benefits for the development of commercial deep space and space faring in general including affordable fast transits to mitigate the human health impacts of galactic cosmic radiation (GCR) and microG. The key to higher than chemical Isp, beyond H2-O2, and beyond the radiated energy and systems limitations of solar for space faring, is a light weight, high energy density source, either on board or via utilization of energy beaming to the vehicle. Traditional energy sources include chemical, heat, electrics, mechanical, photons, and nuclear. Propulsive mass can be carried on board, sourced beyond the surface of Earth via in-situ resource utilization (ISRU), and includes harvesting from atmospheres. High Isp via electromagnetic related propulsive mass acceleration requires sufficient ionization and conductivity. The major metrics for space propulsion are costs, safety, Isp, weight, and thrust level, the latter dependent upon mission requirements. High thrust for human missions is needed to reduce time exposed to radiation and microG and high thrust is required for space access. Costs of space access are reducing via reusability, printing manufacture, and robotization of manufacturing and operation. Chemical rockets provide high thrust from the expansion of the heated mass constituents at high mass flow. The other high thrust propulsion approach is magnetohydrodynamics (MHD), which, in addition to high thrust, has a high Isp of over 2,000 seconds of Isp. The VASIMIR engine offers some 5,000 seconds of Isp at high thrust [ref. 3]. Electric propulsion cycles are capable of Isp much higher than that but at low thrust levels using available energy sources. With cost as a major metric, reusable rockets and improved manufacturing and operations are rapidly greatly lowering the costs of space access, which could provide/supply in space fuel depots and affordable chemical propulsion for the desired human fast transits (e.g., some 200-day round trips to Mars). The other option for fast transits is VASIMIR, given a nuclear on-board energy source that has the requisite many megawatts of power and an alpha, kgs of weight/kW of energy produced, on the order of one (i.e., a light weight, high energy density energy source). The purpose of this report is to examine the options beyond traditional rocket engines, where propulsive mass and energy are combined, specifically the separation of propulsive mass and energy. This report considers the spectrum of advanced energetics, sources of propulsive mass, conductivity enhancement approaches, energy beaming possibilities, and candidate propulsion cycles. Suggestions are made for various combinatorial, system level, beyond traditional combustion rocket, space propulsion approaches for human deep space missions given the changing conditions of increased knowledge of deep space resources for ISRU, revolutionary energetics, and technology advancements writ large.

Dennis M. Bushnell↗

NASA NTRS2023

An MBSE Approach for Developing an Autonomous Rover Platform

The proliferation of increasingly autonomous systems calls for new ways to address how safety is assured. As these systems become more advanced and complex, it becomes more important to model and prototype autonomous functions at the systems level and the functions that assure they are operating safely and as expected. To that effect, researchers at the National Aeronautics and Space Administration (NASA) 's Robust Software Engineering (RSE) group are working on prototyping a Research Autonomous Vehicle, commonly referred to as R-RAV. The R-RAV is an autonomous rover platform designed to act as a case study for assured autonomy research. Moreover, an overarching goal is for the R-RAV to serve as a training ground for other mission projects. In this paper, we will detail how we have used a Model-Based Systems Engineering (MBSE) approach to model a prototype of the R-RAV and test and verify its different functionalities.

MBSE↗

NASA NTRS2022

Lunar Surface Data Book

This document provides a common reference set of existing lunar surface data, products, analytical assumptions, and representative use cases to be incorporated into Artemis surface mission planning efforts. This document pertains solely to the surface of the Moon and the ecosystem of data and products to be used to describe the lunar surface.

Tamra George↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

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.

NLP↗

NASA NTRS2022

Assurance Equations: A Cost and Criticality Model for Optimizing Quality Assurance Surveillance

The cost of quality vs cost of failure correction has been a long-running topic of discussion within the Aerospace community. It leads directly to concepts of “risk tolerance”, and risk-based decision-making. It would be valuable if there was a way to compute the optimal investment in customer-executed quality assurance activities using defect significance with respect to performance objectives, the activities’ defect detection effectiveness, and the cost-penalty for late discovery of impactful defects. This optimization is particularly of interest to projects whose budget constraints significantly limit their risk management options.The cost to fix defects (i.e., failure correction) escalates as the project matures. There have been studies attempting to determine the relative cost of fixing defects discovered during various phases of a project life cycle with important implications, all of which suggest growth factors are large. The commonly referred to 1:10:100 rule represents a cost multiplier for repair/rework across the Design to Fab to Test hardware development phases. Cost premiums for QA activities also accumulate when they are treated as mandatory (due to schedule drag) or are performed later than their assigned phase.This paper describes the modeling of development phase -dependencies in the conduct of typical customer-executed quality assurance activities. Our initial modeling encompasses:• Distinct phases of the production lifecycle• Multiple kinds of Defects, each with some a-priori likelihood of being present• Each defect’s impact on performance Objectives for a type of hardware• The cost and efficacy of assurance techniques at detecting such Defects• The costs of fixing those Defects detected in a given phase of the production lifecycleThe model captures assurance activities’ abilities to Detect defects. Upon detection it is assumed that the Defect is immediately fixed. Defects that “escape” detection by some activity may thereafter be detected by a later activity, but by then the cost of fixing the Defect may have escalated. Defects are related to the performance Objectives they would detract from, were those Defects to remain present in the operating system.We have constructed and are exploring, a model that relates the importance of hardware system elements to mission objectives, the impact of types of Defects on those hardware types, the cost of customer-executed assurance activities (i.e., supplier controls) and their effectiveness towards reducing an impactful quality escape, and the cost of Defect correction across production phase. We describe the approach taken to select the key model aspects, why they are relevant to our NASA mission, and our efforts to populate it with relevant and contemporary data. We use a notional example to illustrate model design and function.

Plante, Jeannette↗

NASA NTRS2023

Lunar Surface Data Book

This document provides a common reference set of existing lunar surface data, products, analytical assumptions, and representative use cases to be incorporated into Artemis surface mission planning efforts. This document pertains solely to the surface of the Moon and the ecosystem of data and products to be used to describe the lunar surface.

Lora Dishongh↗

NASA NTRS2020

A Generalized Guidance Approach to In-Space Solid-Propellant Vehicle Maneuvers

Exploration-class vehicles that require fully autonomous ascent and descent must employ robust, explicit path-adaptive guidance algorithms that can operate in a wide range of physical environments. Vehicle designs that employ solid-propellant rocket motors (SRMs) for maneuvers are attractive from a system engineering perspective because of their simplicity and reliability but may cause complications for both mission designers and GNC engineers when dealing with total impulse uncertainty, as well as proper energy management of a motor with an uncontrolled cutoff time. This paper presents a simplified guidance algorithm, named Simple Cross-Product Steering (SxS), that was derived during early studies of NASA’s Mars Sample Return mission’s Mars Ascent Vehicle. The algorithm takes roots in a flight-proven guidance algorithm commonly referred to as Cross-Product Steering. This algorithm has been shown to provide sufficient guidance accuracy for in-space SRM burns in a simulated Martian environment, and preliminary studies have been conducted to test the algorithm in a solid-propellant lunar braking scenario. A method for predicting proper motor ignition time during execution of the Cross-Product Steering algorithm is the primary contribution of this paper. Mechanization notes are also provided that were realized in early phases of MAV. Results are shown for an example ascent vehicle a simulated Mars environment.

Jason M Everett↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

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.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

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.

NLP↗