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This document describes the Arrival Runway Model (ARM) Machine Learning (ML) service developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
This document describes the Departure Runway Model (DRM) Machine Learning (ML) service developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
This document describes the Estimated ON Model (EON) Machine Learning (ML) services developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
This document describes the Airport Configuration Prediction Model (ACPM) Machine Learning (ML) service developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
This document describes the Impeded (ITIM) and Unimpeded Taxi In Time Prediction Model (UTIM) Machine Learning (ML) services developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
This document describes the Impeded (ITOM) and Unimpeded Taxi Out Time Prediction Model (UTOM) Machine Learning (ML) services developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
Future needs of the National Airspace System require decision support tools to adopt a service-oriented architecture in alignment with the FAA’s vision for an Info-Centric NAS. To achieve this, many existing systems will need to undergo a digital transformation from a monolithic decision support tool to a service-oriented architecture where individual services are exposed through well defined Application Programming Interfaces (APIs). To enable this transformation, NASA has developed the Digital Information Platform as a cloud based foundation for development of aviation services with a special focus towards Artificial Intelligence and Machine Learning (ML) services. This paper describes the work required for the transformation of NASA’s legacy surface management system to a real-time ML based decision support system deployed in the cloud. Details of the Machine Learning Operations (MLOps) infrastructure and best practices are described which enabled the end-toend lifecycle management of ML within an integrated software system. Validation results are provided from an operational field evaluation where performance was benchmarked against the legacy approach.
This paper describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.
This presentation describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.
The path to NAS-wide implementation for ATD-2 multi-airport IADS is as a digital service for flight operators that builds on and supplements FAA TBO investments. This session will focus on a suite of Machine Learning (ML) services the ATD-2 team developed to model airport surface operations. The approach used to leverage SWIM data sets will be described and a preliminary analysis of ML model accuracy will be presented. NASA’s plans to build on this work under the ATM-X Digital Information Platform (DIP) sub-project will be discussed.
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Usage of Machine Learning (ML) algorithms within NASA’s Science Mission Directorates have been increasing over theyears. This can be quantitatively observed in the upward trends of ML usage found by analyzing the publications andpresentations (in affiliation with NASA) available through NASA Technical Reports Server (NTRS) and PubMed Central(PMC). Identifying the problem types and class of ML algorithms used to tackle them across the divisions can presentopportunities for collaborations, interdisciplinary projects and knowledge transfer for sustainable partnerships. In thispresentation, we will present the trend analysis of ML algorithms used in different SMD divisions based on the publicationsand presentations publicly available. We identify these trends by leveraging ML algorithms which are able to search throughthe publication texts semantically; which are also highly scalable. We will also present an analysis on the available opensource tools and services in NASA leveraging AI/ML algorithms. This work will provide ample avenues for collaborativeefforts across different disciplines based on the surfaced trends.
Usage of Machine Learning (ML) algorithms within NASA’s Science Mission Directorates have been increasing over the years. This can be quantitatively observed in the upward trends of ML usage found by analyzing the publications and presentations (in affiliation with NASA) available through NASA Technical Reports Server (NTRS) and PubMed Central(PMC). Identifying the problem types and class of ML algorithms used to tackle them across the divisions can present opportunities for collaborations, interdisciplinary projects and knowledge transfer for sustainable partnerships. In this presentation, we will present the trend analysis of ML algorithms used in different SMD divisions based on the publications and presentations publicly available. We identify these trends by leveraging ML algorithms which are able to search through the publication texts semantically; which are also highly scalable. We will also present an analysis on the available opensource tools and services in NASA leveraging AI/ML algorithms. This work will provide ample avenues for collaborative efforts across different disciplines based on the surfaced trends.
Brief updates of Digital Information Platform (DIP) plans for platform-enabled sustainable aviation services. DIP will provide the capability to integrate key flight information from multiple sources and make it easier to access data critical to developing services using advanced techniques such as machine learning. NASA will share ML-based microservices on the platform for industry to use as reference implementations. The platform will support an ecosystem to share reusable services and promote innovation for digital information.
Rock, air and service water samples were collected for microbial analyses from 3.2 kilometers depth in a working Au mine in the Witwatersrand basin, South Africa. The approx. 1 meter wide mined zone was comprised of a carbonaceous, quartz, sulfide, uraninite and Au bearing layer, called the Carbon Leader, sandwiched by quartzite and conglomerates. The microbial community in the service water was dominated by mesophilic aerobic and anaerobic, alpha, beta and gamma-Proteobacteria with a total biomass concentration approx. l0(exp 4) cells/ ml, whereas, that of the mine air was dominated by members of the Chlorobi and Bacteroidetes groups and a fungal component. The microorganisms in the Carbon Leader were predominantly mesophilic, aerobic heterotrophic, nitrate reducing and methylotrophic, beta and gamma - Proteobacteria that were more closely related to service water microorganisms rather than air microbes. Rhodamine WT dye and fluorescent microspheres employed as contaminant tracers, however, indicated that service water contamination of most of the rock samples was less that 0.01% during acquisition. The microbial contaminants most likely originated from the service water, infiltrated the low permeability rock through and accumulated within mining-induced fractures where they survived for several days prior to being mined. Combined PLFA and terminal restriction fragment length profile (T-RFLP) analyses suggest that the maximum concentration of indigenous microorganisms in the Carbon Leader was less than lo(exp 2) cells/ g. PLFA, S-35 autoradiography and enrichments suggest that the adjacent quartzite was less contaminated and contained -10(exp 3) cells/gram of a thermophilic, sulfate reducing bacteria, SRB, some of who are delta Proteobacteria. Pore water and rock geochemical analyses suggest that these SRB's may have been sustained by sulfate diffusing from the adjacent U-rich, Carbon Leader where it was formed by radiolysis of sulfide.
A flexible multibody dynamics approach is applied to the Space Launch System (SLS) liftoff Coupled Loads Analysis (CLA), enabling the inclusion of a large array of component nonlinearities with extreme computational efficiency. The nonlinearities include the cryogenic induced preloads due to the large rotations of the aft struts connecting the Core Stage (CS) to the boosters, the contact/separation and potential re-contact at the booster aft skirt to Mobile Launcher(ML) interface, contact/separation and potential re-contact at the ML/extensible columns interfaces, secondary disconnect of the CS umbilicals including the LOX and LH2 Tail Service Mast Umbilicals (TSMUs), and the disconnect of the upper stage umbilical, the Interim Cryogenic Propulsion Stage Umbilical (ICPSU). The ICPSU disconnect involves algorithms simulating the winch motors reeling lanyard ropes, the nonlinear modeling of ropes and hoses, the disconnect and capture of multiple umbilical ground plates by catch-nets (geometrically nonlinear models), and the large rotations of the ML gantry in order to track clearances between the lifting SLS vehicle and umbilicals rotating out of the way. The flexible multibody dynamics framework utilized for these simulations provided a systematic and efficient framework for adding complex nonlinearities at the system level which would have otherwise not been possible in standard CLAs or would have to be treated by separate local analyses thereby not accounting for the coupled system behavior.
The suitability and cost effectiveness of using a lightning mast for the shuttle service and access tower (SSAT) similar to the type used for the Apollo Soyuz Test Project (ASTP) mobile launcher (ML) was evaluated. Topics covered include: (1) ASTP launch damage to mast, mast supports, grounded overhead wires, and the instrumentation system; (2) modifications required to permit reusing the ASTP mast on the SSAT; (3) comparative costing factors per launch over a 10 year period in repetitive maintenance and refurbishment of the existing and modified masts, mast supports, grounded overhead wires, and ground instrumentation required to sustain mechanical and electrical integrity of the masts; (4) effects of blast testing samples of the ASTP ML type mast (corrosion and electrical flashover); (5) comparison of damages from ASTP launch and from blast testing.
The demand for voice and data communications continues to rise with the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations throughout the National Airspace System (NAS). Recent studies have shown that the anticipated growing demand for spectrum resources will exceed the capacity of existing aviation spectrum allocations. Further, airspace configurations, via assignment of fixed channel allocations within standard service volumes, do not allow for the dynamic and efficient distribution of spectrum resources based on airspace demand; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating applications of artificial intelligence (AI), machine learning (ML), and other advanced concepts to solve a dynamic constraint satisfaction problem which is analogous to the frequency assignment problem faced by aviation. Procedures and strategies for dynamic channel allocation can be borrowed from other large-scale mobile services (i.e., 4G/5G applications) and can provide a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the strict quality of service prescribed by aeronautical standards.