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NASA NTRS2014

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

Landsat↗

NASA NTRS2016

Automated ISS Flight Utilities

During my internship at NASA Johnson Space Center, I worked in the Space Radiation Analysis Group (SRAG), where I was tasked with a number of projects focused on the automation of tasks and activities related to the operation of the International Space Station (ISS). As I worked on a number of projects, I have written short sections below to give a description for each, followed by more general remarks on the internship experience. My first project is titled "General Exposure Representation EVADOSE", also known as "GEnEVADOSE". This project involved the design and development of a C++/ ROOT framework focused on radiation exposure for extravehicular activity (EVA) planning for the ISS. The utility helps mission managers plan EVAs by displaying information on the cumulative radiation doses that crew will receive during an EVA as a function of the egress time and duration of the activity. SRAG uses a utility called EVADOSE, employing a model of the space radiation environment in low Earth orbit to predict these doses, as while outside the ISS the astronauts will have less shielding from charged particles such as electrons and protons. However, EVADOSE output is cumbersome to work with, and prior to GEnEVADOSE, querying data and producing graphs of ISS trajectories and cumulative doses versus egress time required manual work in Microsoft Excel. GEnEVADOSE automates all this work, reading in EVADOSE output file(s) along with a plaintext file input by the user providing input parameters. GEnEVADOSE will output a text file containing all the necessary dosimetry for each proposed EVA egress time, for each specified EVADOSE file. It also plots cumulative dose versus egress time and the ISS trajectory, and displays all of this information in an auto-generated presentation made in LaTeX. New features have also been added, such as best-case scenarios (egress times corresponding to the least dose), interpolated curves for trajectories, and the ability to query any time in the EVADES output. As mentioned above, GEnEVADOSE makes extensive use of ROOT version 6, the data analysis framework developed at the European Organization for Nuclear Research (CERN), and the code is written to the C++11 standard (as are the other projects). My second project is the Automated Mission Reference Exposure Utility (AMREU).Unlike GEnEVADOSE, AMREU is a combination of three frameworks written in both Python and C++, also making use of ROOT (and PyROOT). Run as a combination of daily and weekly cron jobs, these macros query the SRAG database system to determine the active ISS missions, and query minute-by-minute radiation dose information from ISS-TEPC (Tissue Equivalent Proportional Counter), one of the radiation detectors onboard the ISS. Using this information, AMREU creates a corrected data set of daily radiation doses, addressing situations where TEPC may be offline or locked up by correcting doses for days with less than 95% live time (the total amount time the instrument acquires data) by averaging the past 7 days. As not all errors may be automatically detectable, AMREU also allows for manual corrections, checking an updated plaintext file each time it runs. With the corrected data, AMREU generates cumulative dose plots for each mission, and uses a Python script to generate a flight note file (.docx format) containing these plots, as well as information sections to be filled in and modified by the space weather environment officers with information specific to the week. AMREU is set up to run without requiring any user input, and it automatically archives old flight notes and information files for missions that are no longer active. My other projects involve cleaning up a large data set from the Charged Particle Directional Spectrometer (CPDS), joining together many different data sets in order to clean up information in SRAG SQL databases, and developing other automated utilities for displaying information on active solar regions, that may be used by the space weather environment officers to monitor solar activity. I consulted my mentor Dr. Ryan Rios and Dr. Kerry Lee for project requirements and added features, and ROOT developer Edmond Offermann for advice on using the ROOT library. I also received advice and feedback from Dr. Janet Barzilla of SRAG, who tested my code. Besides these inputs, I worked independently, writing all of the code by myself. The code for all these projects is documented throughout, and I have attempted to write it in a modular format. Assuming that ROOT is updated accordingly, these codes are also Y2038-compliant (and Y10K-compliant). This allows the code to be easily referenced, modified and possibly repurposed for non-ISS missions in the future, should the necessary inputs exist. These projects have taught me a lot about coding and software design - I have become a much more skilled C++ programmer and ROOT user, and I also learned to code in Python and PyROOT (and its advantages and disadvantages compared to C++/ ROOT). Furthermore, I have learned about space radiation and radiation modeling, topics that greatly interest me as I pursue a degree in physics. Working alongside experimental physicists like Dr. Rios, I have developed a greater understanding and appreciation for experimental science, something I have always leaned towards but to which I lacked significant exposure. My work in SRAG has also given me the invaluable opportunity to witness the work environment for physicists at NASA, and what a career in academia may look like at a government laboratory such as NASA Johnson Space Center. As I continue my studies and look forward to graduate school and a future career, this experience at NASA has given me a meaningful and enjoyable opportunity to put my skills to use and see what my future career path might hold.

Offermann, Jan Tuzlic↗

NASA NTRS2017

Development and Validation of an Automated Simulation Capability in Support of Integrated Demand Management

Integrated Demand Management (IDM) is a near- to mid-term NASA concept that proposes to address mismatches in air traffic system demand and capacity by using strategic flow management capabilities to pre-condition demand into the more tactical Time-Based Flow Management System (TBFM). This paper describes an automated simulation capability to support IDM concept development. The capability closely mimics existing human-in-the-loop (HITL) capabilities, automating both the human components and collaboration between operational systems, and speeding up the real-time aircraft simulations. Such a capability allows for parametric studies that will inform the HITL simulations, identifying breaking points and parameter values at which significant changes in system behavior occur. This paper also describes the initial validation of individual components of the automated simulation capability, and an example application comparing the performance of the IDM concept under two TBFM scheduling paradigms. The results and conclusions from this simulation compare closely to those from previous HITL simulations using similar scenarios, providing an initial validation of the automated simulation capability.

Integrated Demand Management↗

NASA NTRS2017

Management by Trajectory Trade Study of Roles and Responsibilities Between Participants and Automation Report

This report describes a trade study of roles and responsibilities associated with the Management by Trajectory (MBT) concept. The MBT concept describes roles, responsibilities, and information and automation requirements for providing air traffic controllers and managers the ability to quickly generate, evaluate and implement changes to an aircraft's trajectory. In addition, the MBT concept describes mechanisms for imposing constraints on flight operator preferred trajectories only to the extent necessary to maintain safe and efficient traffic flows, and the concept provides a method for the exchange of trajectory information between ground automation systems and the aircraft that allows for trajectory synchronization and trajectory negotiation. The participant roles considered in this trade study include: airline dispatcher, flight crew, radar controller, traffic manager, and Air Traffic Control System Command Center (ATCSCC) traffic management specialists. The proposed allocation of roles and responsibilities was based on analysis of several use cases that were developed for this purpose as well as for walking through concept elements. The resulting allocation of roles and responsibilities reflects both increased automation capability to support many aviation functions, as well as increased flexibility to assign responsibilities to different participants - in many cases afforded by the increased automation capabilities. Note that the selection of participants to consider for allocation of each function is necessarily rooted in the current environment, in that MBT is envisioned as an evolution of the National Airspace System (NAS), and not a revolution. A key feature of the MBT allocations is a vision for the traffic management specialist to take on a greater role. This is facilitated by the vision that separation management functions, in addition to traffic management functions, will be carried out as trajectory management functions. This creates an opportunity for flexibility, allowing the traffic management specialist to carry out tasks that today can only be carried out by the controller currently in contact with the aircraft. This additional tasking for the traffic management specialist comes with requirements for workload management. An increased role for the Data-side (D-side) controller relative to the Radar-side (R-side) controller is a potential approach to mitigating workload for the traffic management specialist, as the D-side controller would have similar ability to perform separation management functions in what today might be considered the "trajectory management" timeframe. This analysis did not distinguish between the D-side and R-side controllers since in many cases the R-side controller works unassisted.

Fernandes, Alicia D.↗

NASA NTRS2018

Cooperative Automation Supporting Pilot-Dispatch Negotiation of Enroute Trajectory Change Requests

The advent of advanced technologies in communication, navigation, and surveillance is enabling more integration between the aircraft and the ground systems in managing air traffic operations. As a result, automation has evolved to provide the flight crew, air traffic controllers, and traffic flow managers with capabilities for collaborating on information access, analysis, and decision making. In this paper, we investigate different cooperative schemes between these agents, supported by automation, in managing dynamic trajectory changes while the flight is en route to improve flight and system performance. The analysis was conducted using an abstract cognitive tasking framework to identify trajectory change tasks independently from the agent performing them. Cooperation schemes were then derived by assessing different levels of cooperation on each task between the air and ground agents and their automation. The assessment was based on which automation-supported agent is more capable of performing the task and the expected benefit mechanisms that result from cooperating. The cooperation schemes were compared based on a qualitative, but objective, assessment of the benefits expected from cooperation.

air-ground integration↗

NASA NTRS2018

Automation of Structural Sizing of Aircraft Concepts Under Static Aeroelastic Constraints

This paper presents an automation process for structural sizing of subsonic and supersonic aircraft concepts under static aeroelastic constraints. The automation process starts with an OpenVSP geometry and ends with a PATRAN plot of a NASTRAN solution for static aeroelastic analysis or optimization. ModelCenter is used to integrate all analysis codes with easy-to-use interfaces. Automation tools are developed to streamline the setup process and avoid user errors. Fuel is distributed by solving an optimization problem to match the center of gravity of aircraft at a specified flight condition. Fuel weights are also automatically attached to the structural model as point masses. All other weights used in FLOPS mission analysis (excluding fuselage and wing structural weights) are automatically attached to or smeared on the structural model. For any given OpenVSP geometry and FLOPS analysis data, a static aeroelastic sizing model for NASTRAN analysis can be generated in a couple of hours. The empirical fuselage and wing structural weights from FLOPS are replaced by structural panel weights from the sized finite-element model. Three supersonic and two subsonic aircraft concepts are used to demonstrate the automation process as a physics-based weight estimation tool for aircraft conceptual design.

Li, Wu↗

NASA NTRS2019

Automated Fiber Placement Manufactured Composites for Science Applications

What automated composite laminate manufacturing isWhy automation is of interest in science applicationsHow composite automation is being considered for science instrument applicationsAnd, about test data showing high stiffness materials processed with automation results in reduced material strength while stiffness and coefficient of thermal expansion are mostly unaffected.

Segal, Ken↗

NASA NTRS2019

Evaluation of Technology Concepts for Energy, Automation, and System State Awareness in Commercial Airline Flight Decks

A pilot-in-the-loop flight simulation study was conducted at NASA Langley Research Center to evaluate flight deck systems that (1) provide guidance for recovery from low energy states and stalls, (2) present the current state and expected future state of automated systems, and/or (3) show the state of flight-critical data systems in use by automated systems and primary flight instruments. The study was conducted using 13 commercial airline crews from multiple airlines, paired by airline to minimize procedural effects. Scenarios spanned a range of complex conditions and several emulated causal and contributing factors found in recent accidents involving loss of state awareness by pilots (e.g., energy state, automation state, and/or system state). Three new technology concepts were evaluated while used in concert with current state-of-the-art flight deck systems and indicators. The technologies include a stall recovery guidance algorithm and display concept, an enhanced airspeed control indicator that shows when automation is no longer actively controlling airspeed, and enhanced synoptic pages designed to work with simplified interactive electronic checklists. An additional synoptic was developed to provide the flight crew with information about the effects of loss of flight critical data. Data was collected via questionnaires administered at the completion of flight scenarios, audio/video recordings, flight data, head and eye tracking data, pilot control inputs, and researcher observations. This paper presents findings derived from the questionnaire responses and subjective data measures including workload, situation awareness, usability, and acceptability as well as analyses of two low-energy flight events that resulted in near-stall conditions.

Kramer, Lynda J.↗

NASA NTRSDate not supplied

Automation of the Uncertainty Quantification Process Based on Probability Boxes with DAKOTA

To date, while the use of CFD is prevalent, very few efforts have been undertaken that truly attempt to document all (or even most) of the sources of uncertainty in the simulations. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the design research and engineering community, test and evaluation community, and ultimately certification for flight. This is especially true for hypersonic air-breathing propulsion systems due to the environment, scale, and duration limitations of ground test facilities. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. Hence, a major obstacle that has prevented the adoption of UQ methods for engineering design and development work is the lack of a tool set to automate most (if not all) of the UQ workflow. Towards this end, the SANDIA package DAKOTA (which has been developed to drive both UQ and optimization processes) will be tightly wrapped around the VULCAN-CFD code to automate the uncertainty quantification process. The automated process will be applied to an isolator turbulence model validation exercise that has previously been documented using a manual approach to the UQ process. Hence, the focus of this paper will be documenting the level to which automation can hide the UQ process details from the CFD practitioner rather than the UQ method itself.

CFD↗

NASA NTRS2021

LMI Automated Air Cargo Operations Market Research and Forecast

Air cargo companies and aircraft manufacturers are making significant investments to enable the movement of cargo via various levels of automated aircraft, such as aircraft with simplified operations requiring a pilot, remotely monitored or piloted aircraft, and fully autonomous aircraft. These investments will enable greater utilization of aircraft while unlocking new air markets traditionally served by ground transportation only. Many cargo companies and aerospace experts envision an operating environment where a single pilot can remotely pilot numerous aircraft for significant increases in aircraft utilization. The future operating environment is also expected to include air cargo companies flying smaller aircraft from airports and distribution centers outside of major U.S. cities directly to city centers, avoiding congested roads and increasing the velocity of cargo shipments, particularly those that are high-value, time-sensitive, and security sensitive (e.g., pharmaceuticals). These are just a few benefits and use cases cargo companies and aerospace experts see with the advancement of automated aircraft. To better understand industry’s direction, NASA asked the LMI team to research the forecasted market, timeline, risks, and opportunities for integrating unmanned air cargo vehicles into the National Airspace System (NAS) for the development and prioritization of the NASA Air Traffic Management Exploration’s research portfolio. To begin the market assessment, we gathered data via numerous interviews with key stakeholders and subject matter experts and literature reviews. We then incorporated the data into a custom-developed systems dynamics model and visualization dashboard. The systems dynamics model classifies the size of the market (e.g., overall fleet size of automated aircraft) for four distinct use cases over the next 20 years. The model projects the year in which various types of automated aircraft will enter the commercial cargo market based on our team’s collective research on when the aircraft will become viable due to manufacturing and certification timelines and the lifespan of current, traditional aircraft in service, to name a few factors. While this report defines our team’s estimated timeline of entry and growth, the model is dynamic—it enables NASA users to change variables based on future-year events. If the necessary technology does not mature in accordance with our assumptions, then NASA can change the entry of service point to a future year to evaluate the changes in market size in the out years. This flexibility will be key to deciding when and how NASA should invest in various areas.

air traffic management↗

NASA NTRSDate not supplied

Automated Software for Crewed Spacecraft - Bridging the Gap from Sci Fi to Reality

With a voice command or a few taps on the console, the spacecraft pivots on a dime at high velocity and gently docks to an orbiting space platform. This is the image most people have of the complex software computations and integrated hardware performance necessary for a spacecraft to successfully perform an automated launch, rendezvous, and docking. Today’s reality is that while computer operations are advancing rapidly, science fiction over-simplifies and over-sells current capabilities. This paper discusses the integration of spacecraft computer automation into the operation of one of the United States’ new Commercial Crew vehicles - the Boeing CST-100 Starliner. Lessons learned by the Boeing Mission Operations team, a unique private-public partnership with NASA, from conceptual design through real-time operation of the first test flight will be discussed along with evolution of the system in preparation for the second uncrewed test flight. Focus will center on how operations has learned to use the automated software to their advantage while also knowing how to adjust the automation in response to spacecraft or mission anomalies.

Robert C. Dempsey↗

NASA NTRS2020

Automated Noise Calibration System (VT-1000)

This paper details an automated Noise Source calibration system in development at Jet Propulsion Laboratory, California Institute of Technology (JPL). The paper begins with a discussion on Noise Figure and Excess Noise Ratio (ENR) theory, fundamentals and governing equations. As part of the fundamentals is a discussion of the system’s use of the Y-factor method to obtain accurate measurements of the Unit Under Test (UUT), and how these measurements are compared against a known ENR standard to obtain the UUT’s ENR values. There is also an in-depth discussion on uncertainty quantification for Noise Source system calibrations. The architecture of the automated calibration system is provided, which includes both the system’s hardware and software configuration. The software is written in Python 3, and provides the user detailed instruction on how to proceed, including step-by-step connection requirements. This system automates much of the measurement process, including real-time uncertainty quantification and report generation, as well as real-time feedback to the user to allow intervention if necessary. The system takes advantage of a database of results from previous measurements to compare calibration history of the ENR measurements. The automated system presented here operates over a frequency range from 10 MHz to 50 GHz, and has shown substantial time savings over traditional manual methods of performing this calibration.

Timpe, Scott↗

NASA NTRSDate not supplied

Design and Testing of an Approach to Automated In-Flight Safety Risk Management for sUAS Operations

An onboard risk management automation design is presented based on run-time assurance principles, as well as the concept for In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. The automation is designed to operate independently of the autopilot and perform real-time risk assessment spanning multiple classes of hazards, predict constraint violations, and track autopilot states. In the event of elevated risk conditions or predicted constraint violations, the automation will select from a set of available contingencies and trigger autopilot mode changes if necessary to mitigate risk exposure. The onboard automation also informs the remote operator/pilot of what the independent monitor is observing and any contingency decisions or actions that may arise during flight. Details of an implementation of this design and results of verification and validation activities, as required to meet stringent NASA software and system assurance standards, are also presented. This includes simulation and flight testing using small unmanned aircraft systems.

Ersin Ancel↗

NASA NTRSDate not supplied

Sim to Flight: Evaluating Flight Path Management Automation in High Density Urban Environments

Combined simulation and flight testing enable the study of single- and multi-aircraft performance of onboard automation systems for dynamic flight path management (FPM). The National Aeronautics and Space Administration (NASA) is investigating system performance and functional capabilities of such automation for immersion into complex, high density, future operations such as Urban Air Mobility (UAM). This paper provides an overview of a series of interdependent sim-to-flight research activities involving large-scale batch simulations, human-in-the-loop verification, and flight-test validation of a research prototype FPM automation system. Together, they significantly contributed to a functional assessment of FPM automation functionality in a live-virtual-constructive (LVC) operating environment characterized by two live aircraft and hundreds of virtual aircraft interacting in a modeled complex urban airspace. Initial simulation and flight test results, future work, and conclusions are presented.

Advanced Air Mobility↗

NASA NTRSDate not supplied

Automating Maneuvers: Considerations for Collision Avoidance

As more space operators implement large constellations of spacecraft, automating orbit maintenance maneuvers becomes a key feature of their operations concept to ensure that the workload is manageable. However, the practice of performing a maneuver without sharing the plan with other nearby spacecraft causes a risk that two spacecraft will collide, not only destroying the spacecraft involved, but creating debris that will affect all other spacecraft using that orbit regime. In order to share the maneuver plan, a predicted ephemeris file containing the maneuver must be sent to a central authority to screen against predicted trajectories of all other on-orbit objects to determine where and when close approaches will occur that may need to be mitigated. Currently the screening authority used by US operators is the 19th Space Defense Squadron; screenings are performed once every 8 hours, meaning that spacecraft using automated maneuvering need to allow 16 hours to share their maneuver plan via the screening process in advance of maneuver execution in case one screening is missed and the next needs to be used. In an effort to speed up the screening process to benefit spacecraft using automated maneuvering, a prototype system for performing near-real-time screenings has been developed in support of the NASA Starling mission, a constellation of four cubesats that fly at the same altitude as the SpaceX Starlink constellation. Both of these constellations perform automated maneuvering, so without screening the planned maneuvers before execution, the two constellations would risk a collision. This paper describes the traditional conjunction assessment (CA) process, the prototype real-time CA screening capability, plans for the experiment to test the prototype, and next steps.

conjunction assessment↗

NASA NTRSDate not supplied

Advances in Automation of Overset Structured Volume Mesh Generation and Domain Connectivity

Automation of overset structured surface mesh generation has recently been accomplished by the creation of face, edge, and node meshes based on Boundary Representation solids as the geometry input. The current work continues the automation effort in overset volume mesh generation and domain connectivity based on the auto-generated surface meshes. All near-body curvilinear volume meshes are automatically generated using hyperbolic methods. Automation of this step is enabled by appropriate surface grid point distribution, and selection of boundary-splay and smoothing parameters based on concave and convex surface features. The off-body domain is covered by two automatically generated grid systems. The first contains a single Cartesian mesh with a uniform core enclosing all near-body volume meshes and stretched layers that extend to the far field, while the second consists of a set of small stretched Cartesian grids covering pockets of off-body orphan points. With high quality mesh overlap mostly guaranteed by the surface meshing scheme, orphan points that need to be covered by the second Cartesian mesh system are located away from the fine grid spacing region near the wall. Using line-segment and ray-pierce tests against the surface grids, hole-cutting is accomplished on both near and off-body volume grids resulting in appropriate clearances from the wall. The complete mesh generation automation process is demonstrated on five test cases where flow solutions are also computed and compared with solutions obtained using other methods.

TTT↗

NASA NTRSDate not supplied

Automating Maneuvers: Considerations for Collision Avoidance

conjunction assessment↗

NASA NTRSDate not supplied

Design for Manufacturing of Structures with Automated Fiber Placement via Integration of Analysis and Process Planning

Under the Design for Manufacturing (DFM) task in the NASA HiCAM program, significant process has been made towards establishing a fully automated optimization process that spans the structural analysis, design, and manufacturing process planning for Automated Fiber Placement (AFP) structures. Previous efforts in this area established the data formats and mapping processes needed to exchange data between disciplines. The software tools used in the framework are HyperX (structural optimization), CAPP (process planning), and VCP (AFP path generation). The work has culminated with automation of data exchanges and an optimization process that drives the three software toward convergence. Within this framework, HyperX is used to generate mass-optimum composite designs which are iterated with VCP and CAPP to improve manufacturability of the designs. As the ply manufacturing process is adjusted by CAPP and VCP, HyperX continually re-assesses the structural integrity of the part and makes adjustments to the laminate as needed. For example, the position of fiber steering and tow overlaps/gaps can necessitate changes to the laminate design. Once the design converges, the end result is a stack of plies that meets both structural and manufacturing requirements. This paper presents the details of the framework and demonstrations the automated process on several parts with significant double-curvature.

HiCAM↗