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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Rapid Trajectory Prediction for a Fixed-Wing UAS in a Uniform Wind Field with Specified Arrival Times

This paper presents an algorithm to rapidly generate trajectories for a kinematic fixed-wing Unmanned Aircraft System (UAS) model flying at constant altitude in a uniform wind field. Arrival times are specified by operators and rapid generation is accomplished via an elliptic integral problem formulation. Simulations are provided that illustrate this approach in the context of NASA's UAS Traffic Management Project.

trajectory↗
NASA NTRS2018

Flight Deck Implications for the Implementation of an Integrated Arrival, Departure, and Surface (IADS) Traffic Management System

NASA's Airspace Technical Demonstration-2 (ATD-2) Integrated Arrival, Departure, and Surface (IADS) traffic management system integrates strategic and tactical scheduling tools for traffic sequencing from the gate to the overhead stream and back again for multi-airport, metroplex environments. The system is expected to increase airport efficiency, predictability, and throughout while improving information sharing among all airport operators. A series of knowledge elicitation sessions were conducted with commercial airline pilots and general aviation pilots to characterize airport operations and procedures. This presentation describes ATD-2 IADS-enabled Information Sharing impacts on the Flight Deck, benefits of that Information Sharing, pilot procedural changes, our pilot engagement/outreach efforts, use of a Mobile App to faciliate information flow in GA operations, and future IADS efforts at other airports/metroplexes.

IADS↗
NASA NTRS2018

ATD-2 Integrated Arrival, Departure, and Surface (IADS) Operations

The ATD-2 Integrated Arrival, Departure, and Surface (IADS) traffic management system extends integrated traffic sequencing all the way from the gate to the overhead stream and back again for multi-airport, metroplex environments. NASA and the FAA are developing the IADS system in close coordination with industry partners.

pilot procedures↗
NASA NTRS2018

Autonomous Surface Site Establishment to Ensure Safe Crew Arrival and Operations

Traditional human Mars missions have relied on crew to support the surface systems. However, for safety, the surface systems will likely need to be setup and capable of operating prior to the arrival of crew. To mitigate risks to the crew, a novel surface architecture has been developed that addresses risks associated with other Mars missions. This architecture relies on a reusable descent and ascent vehicle, extensive in-situ resource utilization, redundant habitation systems, and emerging autonomous capabilities. The resulting surface architecture increases safety for the crew while also providing potential to expand to support longer missions with larger populations in the future.

Jones, Christopher A.↗
NASA NTRS2019

Models of Terminal Arrival Efficiency Rate

Terminal Arrival Efficiency Rate (TAER) is a measure of TRACON performance and the impact of Traffic Management Initiatives (TMI) within 100 miles of the airport. This measures not just the approach control performance, but also ARTCC performance to deliver traffic evenly. Identifying conditions that lead to low TAER can allow development of method to improve it. In this study, we developed linear and decision tree models of TAER.

Air Traffic Management↗
NASA NTRS2019

Airspace Technology Demonstration 2 (ATD-2): Integrated Arrival/ Departure/Surface (IADS) System Demonstration Technology Transfer Document Summary (ReadMe) Version 2 - Phase 2 Fused IADS

Airspace Technology Demonstration 2 (ATD-2) is part of NASA's Airspace Technology Demonstrations (ATD) Project under its Airspace Operations and Safety Program (AOSP). ATD-2 is a multi-year research and development effort to improve the predictability and operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure, and surface prediction, scheduling, and management systems. In order to ensure that the products of this knowledge and technology transfer are relevant and useful, NASA has created strong partnerships with the FAA and industry stakeholders. This summary document and accompanying artifacts satisfy the second of three research transition products (RTPs) defined in the IADS research transition team (RTT) plan. This transfer consists of NASA's ATD-2 Phase 1 Baseline IADS and Phase 2 Fused IADS system capabilities

Capps, Alan↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗
NASA NTRS2021

Airspace Technology Demonstration 2 (ATD-2): Integrated Arrival/ Departure/Surface (IADS) System Demonstration - Technology Transfer Document Summary (ReadMe)Version 3 (Final Phase 3 Metroplex IADS)

Airspace Technology Demonstration – 2 (ATD-2) is part of NASA’s Airspace Technology Demonstrations (ATD) Project under its Airspace Operations and Safety Program (AOSP). ATD-2 is a multi-year research and development effort to improve the predictability and operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure, and surface prediction, scheduling, and management systems. In order to ensure that the products of this knowledge and technology transfer are relevant and useful, NASA has created strong partnerships with the FAA and industry stakeholders. This summary document and accompanying artifacts satisfy the final delivery of research transition products (RTPs) defined in the IADS research transition team (RTT) plan.

ATD-2↗
NASA NTRS2022

Preparing the Mars Relay Network for the Arrival of the Perseverance Rover at Mars

The Perseverance rover represents NASA’s latest achievement in Mars exploration. Landing successfully on 18 Feb 2021, the rover’s transmitted data during its entry, descent, and landing (EDL) were captured by the Mars Reconnaissance Orbiter (MRO) and the Mars Atmosphere and Volatile Evolution (MAVEN) orbiter. This data, broadcast in near-realtime to the world, allowed everyone to share in the excitement (and “terror”) of the day. The images returned thereafter included the first images of the new landing site, video of the landing itself taken from a variety of vantage points, and eventually the historic images of the first powered flight on another planet. Behind the scenes, the return of that data to Earth was accomplished via Mars orbiters operated by NASA and ESA, using three different ground tracking networks. Considered together, this Mars Relay Network (MRN) enabled the successful, timely, and unobtrusive return of the rover’s data. This paper describes the preparations taken by the participants of the MRN in anticipation of the arrival of Perseverance at Mars. These were not only focused on successfully acquiring the rover’s critical event telemetry during its EDL, but also on readying the network to return the rover’s data on an ongoing basis as it pursued its mission objectives. Included is a brief description of the MRN, which represents a highly successful international collaboration and continues as critical infrastructure for NASA’s and ESA’s ongoing Mars exploration. Also summarized are the activities performed prior to EDL, including landing site reconnaissance and mission test and training activities; those activities performed on EDL day, especially the recording, return, and processing of the rover’s critical event telemetry; and those activities that are now being performed on an ongoing basis during the rover’s surface operations, including an outline of the planning processes that enable relay services. Finally, a description of the performance of the network to-date on behalf of the Perseverance rover is given, summarizing the success of the network to provide support to both it and other spacecraft on the surface of Mars.

Srnka, Evan↗