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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
NASA NTRS2021

Integrated Demand Management Techport Closeout

Over the period 2016 to 2020, Integrated Demand Management (IDM) concept, procedures, and tools have been developed - first for clear-weather and a single airport constraint and then expanded to handle a multi-constraint problem during convective weather at Newark (EWR), LaGuardia (LGA) and Philadelphia (PHL) airports. The concept was evaluated in a series of human-in-the-loop simulations to confirm concept benefits in terms of better schedule predictability, reduction of delays, and increased throughput, especially during convective weather. In addition, other conditions with mixed participation of multi-trajectories from the airlines were evaluated that showed significant benefits to the individual airlines as well as the overall traffic flow. The IDM concept was initiated in the SMART-NAS project, was completed under the ATM-X project, and was developed collaboratively with the FAA and airline partners. Over the course of IDM development, NASA researchers produced 19 conference papers and publications. Outside supporting organizations, funded by IDM, produced 20 additional conference papers and publications, in which they advanced fundamental research on topics such as better stochastic traffic demand prediction, and application of machine learning techniques for modeling traffic management initiatives. Final concept procedures and tool specifications have been transferred to the FAA Air Traffic Organization Operational Concepts, Validation, and Requirements group.

William N Chan↗
NASA NTRSDate not supplied

Design of Electrostatic Dust Lofting Suborbital Flight Experiment Examining Photoionization under Lunar Gravity

Dust on the lunar surface electrostatically charges due to the plasma environment surrounding the Moon, causing grains to become lofted and adhere to nearby surfaces including landers and astronauts. Studying the behaviors of these charged particles in the lunar environment is essential to plan around the deleterious effects of dust to future Moon missions. Models attempt to predict the amount of dust loading that can be expected in many of these scenarios, but they require experimental validation to be predictive. This physics cannot be fully studied on Earth due to the six times larger gravitational force obscuring the electrostatic interactions, so it is necessary to run experiments in a more relevant environment, including vacuum and near-lunar gravitational effects. An experiment has been designed to fly on the Lunar Gravity Acceleration (LGA) mission aboard the Blue Origin New Shepard suborbital rocket. This experiment will perform photoionization charging of lunar regolith simulant grains under the illumination of an ultraviolet (UV) source. As a result, the charged grains will then electrostatically repel one another and loft in the reduced gravity environment; their trajectories will be imaged via a high-speed camera. Preliminary laboratory results influencing the design of this experiment will be presented, including characterization of several UV sources, measurements of photoionization currents under various vacuum conditions, and examination of lunar simulant dust lofting under terrestrial gravity. Results from this flight will be compared with ground-based testing and the laboratory results outlined above to examine the dependence on gravity and will be fed into the dust charging and lofting models currently under development.

electrostatics↗