Search NASA⌕ Search

SEARCH · Search NASA

Results for “aviation data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 deg to 90 deg. 1: High-wing model B

Aerodynamic characteristics obtained in a rotational flow environment utilizing a rotary balance located in a spin tunnel are presented in plotted form for a 1/6.5 scale, single engine, high wing, general aviation airplane model. The configurations tested included the basic airplane, various wing leading-edge devices, tail designs, and rudder control settings as well as airplane components. Data are presented without analysis for an angle of attack range of 8 deg to 90 deg and clockwise and counter-clockwise rotations covering an omega b/2V range from 0 to 0.85.

Bihrle, W., Jr.↗
NASA NTRS1979

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 deg to 90 deg. 2: Low-wing model B

Aerodynamic characteristics obtained in a rotational flow environment utilizing a rotary balance located in the spin tunnel are presented in plotted form for a 1/6.5 scale, single engine, low wing, general aviation airplane model. The configurations tested included the basic airplane, various wing leading-edge devices, tail designs, and rudder control settings as well as airplane components. Data are presented without analysis for an angle-of-attack range of 8 deg to 90 deg and clockwise and counter-clockwise rotations covering an (omega)(b)/2V range from 0 to 0.85.

Bihrle, W., Jr.↗
NASA NTRS1982

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 deg to 90 deg. 2: Influence of horizontal tail location for Model D

The influence of horizontal tail location on the rotational flow aerodynamics is discussed for a 1/6-scale general aviation airplane model. The model was tested using various horizontal tail positions, with both a high and a low-wing location and for each of two body lengths. Data were measured, using a rotary balance, over an angle-of-attack range of 8 to 90 deg, and for clockwise and counter-clockwise rotations covering an Omega b/2V range of 0 to 0.9.

Barnhart, B.↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS1983

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 deg to 90 deg. 1: Influence of airplane components for model D

The influence of airplane components, as well as wing location and tail length, on the rotational flow aerodynamics is discussed for a 1/6 scale general aviation airplane model. The airplane was tested in a built-up fashion (i.e., body, body-wing, body-wing-vertical, etc.) in the presence of two wing locations and two body lengths. Data were measured, using a rotary balance, over an angle-of-attack range of 8 deg to 90 deg, and for clockwise and counter-clockwise rotations covering an omega b/2V range of 0 to 0.9.

Ralston, J.↗
NASA NTRS1975

A review of the NASA V-G/VGH general aviation program

The V-G and VGH data collected from a wide variety of general aviation airplanes since the inception of the NASA V-G/VGH General Aviation Program in 1962 are presented. These data were analyzed to obtain information on the gust and maneuver loads, on the operating practices, and on the effects of different types of operations on these parameters.

Jewel, J. W., Jr.↗
NASA NTRS1980

Remotely piloted vehicles. Citations from the International Aerospace abstracts data base

These citations from the international literature cover various aspects of remotely piloted vehicles. Included are articles concerning aircraft design, flight tests, aircraft control, cost effectiveness, automatic flight control, automatic pilots, and data links. Civil aviation applications are included, although military uses of remotely piloted vehicles are stressed. This updated bibliography contains 224 citations, 43 of which are new additions to the previous edition.

Mauk, S. C.↗
NASA NTRS1987

Simulator systems integration

The implementation of available wind shear data into general aviation flight training simulators is discussed. Currently, there are 11 simulators with wind shear models installed involving some 9 different aircraft models. Retrofits to other systems that were put out earlier are currently underway, and all the new simulators will have wind shear available for the instructors to use for demonstrations and training. There are three types of computer systems involved, and two different types of instructor stations. Most of the systems with wind shear are the CRT-type displays. The integration of wind shear models in flight simulators is discussed in detail.

Shipman, Keith W.↗
NASA NTRS2002

A Low LET Radiation Spectrometer for Measuring Particle Doses in Space and Aircraft

This paper presents experimental data that demonstrates the feasibility of fabricating a miniature nuclear particle dosimeter for monitoring doses in aircraft and satellites. The basic instrument is a Low Linear-Energy-Transfer (LET) Radiation Spectrometer (LoLRS) that is designed to measure the energy deposited by particles with low LET values. The heart of the instrument is a Silicon-Lithium Drifted Diode (SLDD). Test results show that the LoLRS can be used to monitor the radiation threat to personnel in flights of space- and aircraft and also to generate a comprehensive data base from aviation and satellite measurements that can contribute to the formulation of more accurate environmental radiation models for dose predictions with reduced uncertainty factors.

Stassinopoulos, E. G.↗
NASA NTRS2013

Spectrum for UAS Control and Non-Payload Communications

There is an increasing need to fly UAS in the NAS to perform missions of vital importance to National Security and Defense, Emergency Management, and Science as well as commercial applications (e.g. cargo transport). To enable integration of UAS into the National Airspace System, several critical technical barriers must be eliminated, including: Separation Assurance/Sense and Avoid - the uncertainty surrounding the ability to interoperate in ATC environments and maintain safe separation from other aircraft in the absence of an on-board pilot. Human Systems Integration - lack of standards and guidelines with respect to UAS display information as well as lack of Ground Control Station (GCS) design requirements to operate in the NAS. Certification - lack of airworthiness requirements and safety-related data specific to the full range of UAS, or for their avionics systems or other components. Communications - lack of standard, certifiable data links and aviation safety spectrum to operate such links for civil UAS control communications.

Kerczewski, Robert J.↗
NASA NTRS2019

Artificial Intelligence in Aviation Safety Applications - Exploring Myths and Truths of AI and ML

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as ways to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques.Panel speakers will present machine learning approaches they have developed on a variety of aviation data, including digital flight data, safety reporting data, and voice communications data. They will discuss the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel will also discuss common pitfalls in developing an AI solution, the dangers of the current hype around AI, tips for gaining value from a ML solution, how to determine whether a ML approach is appropriate for a problem, and more.

Reeves, Scott (Capt.)↗
NASA NTRS1993

Aviation Weather Program (AWP)

The Aviation Weather Program (AWP) combines additional weather observations, improved forecast technology, and more efficient distribution of information to pilots, controllers, and automated systems to improve the weather information provided to the air traffic control system, pilots, and other users of aviation weather information. Specific objectives include the needs to: improve airport and en-route capacity by accurate, high resolution, timely forecasts of changing weather conditions affecting airport and en-route operations; improve analyses and forecasts of upper-level winds for efficient flight planning and traffic management; and increase flight safety through improved aviation weather hazard forecasting (e.g. icing, turbulence, severe storms, microbursts, or strong winds). The AWP would benefit from participation in a cooperative multiscale experiment by obtaining data for: evaluation of aviation weather forecast products, analysis of four dimensional data assimilation schemes, and experimental techniques for retrieving aerosol and other visibility parameters. A multiscale experiment would also be helpful to AWP by making it possible to evaluate the added benefit of enhanced data sets collected during the experiment on those forecast and analysis products. The goals of the Coperative Multiscale Experiment (CME) are an essential step in attaining the long-term AWP objective of providing two-to-four hour location-specific forecasts of significant weather. Although the possibility of a funding role for the AWP in the CME is presently unclear, modest involvement of Federal Aviation Administration (FAA)/AWP personnel could be expected.

Foote, Brant↗
NASA NTRS1979

A Demonstration Advanced Avionics System for general aviation

A program initiated within NASA has emphasized the use of a data bus, microprocessors, electronic displays and data entry devices for general aviation. A Demonstration Advanced Avionics System (DAAS) capable of evaluating critical and promising elements of an integrating system that will perform the functions of (1) automated guidance and navigation; (2) flight planning; (3) weight and balance performance computations; (4) monitoring and warning; and (5) storage of normal and emergency check lists and operational limitations is described. Consideration is given to two major parts of the DAAS instrument panel: the integrated data control center and an electronic horizontal situation indicator, and to the system architecture. The system is to be installed in the Ames Research Center's Cessna 402B in the latter part of 1980; engineering flight testing will begin in the first part of 1981.

Denery, D. G.↗
NASA NTRS2019

Application of Machine Learning Techniques to Aviation Operations: Promises and Challenges

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. 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. This paper compares the methodology used in and issues to be addressed in applying either model-driven or data-driven methods. Some aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for data-driven methods. The application of MLT to aviation operations falls into three categories: (a) based on the lack of a physics-based model, MLT is the favored approach, (b) marginal difference between regression methods using physics-based models and MLT and (c) better results using a blend of physics-based methods combined with MLT. 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

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗
NASA NTRS2020

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗