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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗
NASA NTRSDate not supplied

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗
NASA NTRS1989

Computerized inspection of gear tooth surfaces

An approach is proposed that uses coordinate measurements of the real surface of spiral bevel gears to determine the actual machine tool setting applied during the gear manufacturing process. The deviations of the real surface from the theoretical one are also determined. Adjustments are then applied by machine tool corrections to minimize these surface deviations. This is accomplished by representing the real surface analytically in the same Gaussian coordinates as the theoretical surface.

Litvin, Faydor L.↗
NASA NTRS1993

On Machine Capacitance Dimensional and Surface Profile Measurement System

A program was awarded under the Air Force Machine Tool Sensor Improvements Program Research and Development Announcement to develop and demonstrate the use of a Capacitance Sensor System including Capacitive Non-Contact Analog Probe and a Capacitive Array Dimensional Measurement System to check the dimensions of complex shapes and contours on a machine tool or in an automated inspection cell. The manufacturing of complex shapes and contours and the subsequent verification of those manufactured shapes is fundamental and widespread throughout industry. The critical profile of a gear tooth; the overall shape of a graphite EDM electrode; the contour of a turbine blade in a jet engine; and countless other components in varied applications possess complex shapes that require detailed and complex inspection procedures. Current inspection methods for complex shapes and contours are expensive, time-consuming, and labor intensive.

Resnick, Ralph↗
NASA NTRS1993

Application of an On-machine Gage for Diameter Measurements

This paper describes the design analysis and application of a laser based gage made specifically for measuring parts on the machine tool to a high accuracy. The tri-beam gage uses three beams of light to measure the local curvature of the part in a manner similar to a V-block gage. The properties of this design include: calibration that is independent of the machine tool scales, non-contact damage free operation, low cost of the gage, and the ability to measure parts in motion.

Harding, Kevin G.↗
NASA NTRS1986

Material for Fast Cutting

New material for cutting tools increases productivity of machining processes. Material, called Iscanite (or equivalent), based on silicon nitride contains more than 90 percent silicon. Combines impact resistance close to that of coated carbides with heat and wear resistance close to those of aluminum oxide ceramics. Material used for cutting on old or new machine tools and makes it possible to exploit fully power and speed of machine.

Perez, A.↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS1980

Method and tool for machining a transverse slot about a bore

A method and apparatus for cutting a transverse slot about a bore of smaller diameter than that of the slot are disclosed. The invention consists of introducing a cutting head facing transversely to the bore, through the bore opening its distance from the mill shaft being progressively extended by the addition of spacers between the head and the shaft until the desired slot depth is obtained. The spacers are held in position by a cable passing from the cutting head through the series of spacers and out along the mill shaft. The mill shaft carrying the cutting head is moved transversely into the object wherein the slot is being cut as the object is being rotated thereabout by the mill table to which it is affixed.

David-Malig, M. A.↗
NASA NTRS1988

Automatic Inspection During Machining

In experimental manufacturing process, numerically-controlled machine tool temporarily converts into inspection machine by installing electronic touch probes and specially-developed numerical-control software. Software drives probes in paths to and on newly machined parts and collects data on dimensions of parts.

Ransom, Clyde L.↗
NASA NTRS1994

Optically Aligned Drill Press

Precise drill press equipped with rotary-indexing microscope. Microscope and drill exchange places when turret rotated. Microscope axis first aligned over future hole, then rotated out of way so drill axis assumes its precise position. New procedure takes less time to locate drilling positions and produces more accurate results. Apparatus adapted to such other machine tools as milling and measuring machines.

Adderholdt, Bruce M.↗
NASA NTRS1972

Thermal analog device reduces machining errors

Thermal analog devices predict thermal expansion and contraction of machine structures subjected to various heat inputs. Analog devices correct positioning of machine tools to compensate for distortion of machine frame.

Mcclure, E. R.↗