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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Observations on the Application of Machine Learning Techniques to Aviation Operations

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 are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories: (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection 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↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: A Case Study

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 are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories 58; (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection 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↗
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↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32124-DND

The NASA DEVELOP National Program seeks to simultaneously build capacity to use Earth observations in early career and transitioning professionals while building capacity with institutional partners to apply Earth observations in conducting operations, making decisions, or informing policy. This is done through 10-week feasibility projects, conducted by the DEVELOP teams in collaboration with decision makers. The program carries out 60-80 projects each year, engaging with over 120 partners from local, state, and federal governments to academic institutions and NGOs. To best understand project partner needs, projects begin with a thorough proposal development process in which partners share their current practices, needs, and capabilities. Throughout the project life cycle, the DEVELOP teams engage with the partners to ensure communication, feedback, and understanding. DEVELOP’s model of conducting rapid feasibility projects is an effective way to show decision maker show they can use NASA Earth science data in new ways to help them make informed decisions. Because the projects are conducted by a DEVELOP team in collaboration with end users, the partners are introduced to new data products and methodologies that they otherwise might not be able to explore within their own resource constraints. This presentation will discuss project examples, success stories, and best practices for engaging with decision makers on applied science projects.

Amanda Clayton↗
NASA NTRSDate not supplied

Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in Air Traffic Management (ATM). 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 databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. 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 promises and challenges in applying MLT to ATM is traced through three examples based on the authors’ experience, each separated by a decade, to show the influence of data and feature selection in the successful application of MLT to ATM. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Machine Learning Techniques↗
NASA NTRSDate not supplied

Simulation and Modeling Concepts for Secure Airspace Operations

With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. This paper examines cyber security vulnerabilities of Urban Air Mobility operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, thus, improving the cyber resiliency of the current and future air traffic management system.

Cybersecurity↗
NASA NTRS2021

Simulation and Modeling Concepts for Secure Airspace Operations

This paper examines cyber security vulnerabilities of Urban Air Mobility operations. With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, and the cyber resiliency of the current and future air traffic management system.

Urban Air Mobility↗
NASA NTRS2021

Simulation and Modeling Concepts for Secure Airspace Operations

This paper examines cyber security vulnerabilities of Urban Air Mobility operations. With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, and the cyber resiliency of the current and future air traffic management system.

Urban Air Mobility↗
NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗
NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗
NASA NTRSDate not supplied

SPHINX: An SEP Model Validation Infrastructure developed through Community Challenges and the SEP Scoreboards

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars (Whitman et al 2022). For these reasons, SEP modelers have developed a rich and diverse set of models with a wide variety of aims. Some models probe the basic physics at the heart of particle acceleration and transport. Others produce fast statistical forecasts or employ disruptive new techniques like Machine Learning with the goal to assist end users in making operational decisions. To enable a consistent and quantitative understanding of SEP model performance, a generalized, automated validation infrastructure, called SPHINX, is being developed at NASA SRAG in close collaboration with NASA CCMC, NASA M2M, NOAA SWPC, and BIRA-IASB. This infrastructure has been built up through a multi-year community challenge. Starting in 2018 at the SHINE workshop, an effort was launched through SHINE, ISWAT, and ESWW to encourage quantitative, comprehensive, and consistent validation of SEP models. This effort has defined a set of challenge SEP events with the aim of generating quantitative comparisons between forecasts and observations and a set of challenge “non-events” to assess false alarms. In 2023, these challenge lists have been extended to statistically significant numbers with a prescribed set of rules for producing forecasts and supported through the dedicated SEPVAL working meetings. The participation of the research community has allowed the infrastructure to validate all the types of outputs being produced by SEP models. In parallel, the SPHINX code is being applied to real time forecasts submitted to the SEP Scoreboards, ensuring that the validation infrastructure can interpret forecasts produced in an operational scenario and provide metrics meaningful for operations. Upon completion, SPHINX and its interactive user interface, SPHINX-Web, will be made available for public use.

space weather↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

Siriwardane, Hema↗
NASA NTRS1996

The Influence of Future Command, Control, Communications, and Computers (C4) on Doctrine and the Operational Commander's Decision-Making Process

Future C4 systems will alter the traditional balance between force and information, having a profound influence on doctrine and the operational commander's decision making process. The Joint Staff's future vision of C4 is conceptualized in 'C4I for the Warrior' which envisions a joint C4I architecture providing timely sensor to shoot information direct to the warfighter. C4 system must manage and filter an overwhelming amount of information; deal with interoperability issues; overcome technological limitations; meet emerging security requirements; and protect against 'Information Warfare.' Severe budget constraints necessitate unified control of C4 systems under singular leadership for the common good of all the services. In addition, acquisition policy and procedures must be revamped to allow new technologies to be fielded quickly; and the commercial marketplace will become the preferred starting point for modernization. Flatter command structures are recommended in this environment where information is available instantaneously. New responsibilities for decision making at lower levels are created. Commanders will have to strike a balance between exerting greater control and allowing subordinates enough flexibility to maintain initiative. Clearly, the commander's intent remains the most important tool in striking this balance.

Mayer, Michael G.↗
NASA NTRSDate not supplied

Spaceflight Medical Evacuation Risk Assessment Principles - A Qualitative Investigation

BACKGROUND Future human space exploration beyond Low Earth Orbit (LEO) will require innovative solutions in many areas, primarily those that provide direct medical support to crew on long-duration missions and optimize their health and performance. The associated challenges therein will be numerous, including but not necessarily limited to extended one-way or "asynchronous" communication delays, minimal to non-existent resupply, and a prolonged transit time to "definitive care" ranging from 3-days to 9-months. At the same time, long-duration exploration spacecraft and crew will face restrictions on mass, power, volume, and data far more significant than that seen in current LEO settings. Given these limitations, medical risk assessment is of primary importance, especially evaluating the implications of a medical evacuation of an ill or injured crewmember. Such evacuations are complicated, potentially dangerous, and well may be impossible in certain phases of the mission. Regardless, such issues must be weighed against the risks of the injured crew remaining aboard a spacecraft with limited medical resources. OBJECTIVE This qualitative study drew from the experiences of subject matter experts (SMEs) in spaceflight and appropriate analog environments (i.e., military, disaster, and extreme environment fields) to identify unique principles common amongst medical evacuation considerations helpful in informing future risk assessment tools. Appropriate analog environments included austere operational settings where multiple factors (weather, logistics/limited resupply, denied/extreme environments, and patient condition) resulted in a limited ability to provide definitive local medical care. The fundamental principles in question revolved around scenarios where evacuation became a complicating yet necessary consideration and where life-threatening medical concerns had to be weighed against critical mission objective(s). The primary authors collected semi-structured data gathered through in-depth interviews with 16 subject matter experts (SMEs). Interview questions investigated how these SMEs consider and weigh the attendant risks present in medical evacuation scenarios. Among the critical questions posed were those that sought to understand how the SME balanced the challenges and requirements of medical evacuation (or keeping an injured patient "on-site" aka: "prolonged field care") against the evacuation operation's risks on impacting overarching mission success. The team analyzed interview transcripts for common themes and principles using the qualitative methods of thematic analysis based on consensus, co-occurrence, and comparison. As a result, nine primary risk consideration themes and nine contributing factor themes emerged, all of which will ideally inform future medical evacuation decision-making tools and operational decision-making for exploration class missions. Specific aims for this study included: 1. Identification of common principles used to assess risks and benefits of medical evacuations in extreme environments 2. Identification of common points of friction or complication and challenges in extreme environment evacuations

A T Almand↗
DOE OSTI2026

Proposed Critical Thinking Metrics for Power Grid Trainees

The TRS Intro to Critical Thinking for Operational Decision-Making course for power grid operators seeks to help trainees improve their use of critical thinking skills in operation environments. To determine the effectiveness of this training, we have identified 3 categories of evaluation measures: critical thinking metrics, situation awareness (SA) evaluations, and behavioral observations. Critical thinking metrics seek to directly measure trainee’s ability to use critical thinking skills through their verbalized responses to different questions or scenarios. SA will be evaluated during various points throughout simulated exercises completed during the training. SA evaluations aim to determine the level of a trainee’s ability to perceive, comprehend and anticipate events in the operational environment. This construct will be assessed through verbalized responses to questions informed by the Situation Awareness Global Assessment Technique (SAGAT). Behavioral observations capture how trainees use the system, including which simulator displays or windows they use or actions they perform and in what order. These observations help us understand how the critical thinking training may inform operator action. We hypothesize that while these measures are correlated with each other, they provide different perspectives on each trainee’s ability to gain SA and use critical thinking skills in operation environments. By comparing these measures in a pre-assessment and a post-assessment, we can determine how much improvement a trainee gains through the training. These measures may also be used during the training as appropriate. This document specifies these 3 categories of measures, which components of these measures currently seem to be the best candidates to use in the training course, and which parts of the training course is related to each component.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2018

Towards Design Principles for Visual Analytics in Operations Contexts

Operations engineering teams interact with complex data systems to make technical decisions that ensure the operational efficacy of their missions. To support these decision-making tasks, which may require elastic prioritization of goals dependent on changing conditions, custom analytics tools are often developed. We were asked to develop such a tool by a team at the NASA Jet Propulsion Laboratory, where rover telecom operators make decisions based on models predicting how much data rovers can transfer from the surface of Mars. Through research, design, implementation, and informal evaluation of our new tool, we developed principles to inform the design of visual analytics systems in operations contexts. We offer these principles as a step towards understanding the complex task of designing these systems. The principles we present are applicable to designers and developers tasked with building analytics systems in domains that face complex operations challenges such as scheduling, routing, and logistics.

Lombeyda, Santiago↗