SEARCH · Search NASA

Results for “IMPACT TESTING MACHINE”

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.

SourceResearch connectionFrom yearThrough year

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

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Recent NASA Research into the Psychoacoustics of Urban Air Mobility Vehicles

NASA maintains multiple psychoacoustic laboratories across the United States. One focus of research in these facilities is to investigate the human reaction to the noise of notional UAM vehicles. This effort will look for answers to several questions: What does a “good” UAM sound like? What might be the impact on existing urban soundscapes? What will people think when there are an [economically viable] number of these machines operating? Although this effort is ongoing, a number of advances have already been made: A psychoacoustic test to guide UAM sound design has been recently completed. Several algorithms to assess the audibility of a UAM vehicle in an existing background soundscape have been put forward. A technical memorandum detailing best practices for recording and reproduction of background sounds for UAM psychoacoustic research has been published (TM 20210017504). Flexible data analysis methods that could be agile to changes in perception that occur when large numbers of vehicles are flying have been developed by extending existing metrics. This presentation gives an overview of this completed and continuing research.

UAM↗

NASA NTRS2004

Durability Assessment of Gamma Tial

Gamma TiAl was evaluated as a candidate alloy for low-pressure turbine blades in aeroengines. The durability of g-TiAl was studied by examining the effects of impact or fretting on its fatigue strength. Cast-to-size Ti-48Al-2Cr-2Nb was studied in impact testing with different size projectiles at various impact energies as the reference alloy and subsequently fatigue tested. Impacting degraded the residual fatigue life. However, under the ballistic impact conditions studied, it was concluded that the impacts expected in an aeroengine would not result in catastrophic damage, nor would the damage be severe enough to result in a fatigue failure under the anticipated design loads. In addition, other gamma alloys were investigated including another cast-to-size alloy, several cast and machined specimens, and a forged alloy. Within this Ti-48-2-2 family of alloys aluminum content was also varied. The cracking patterns as a result of impacting were documented and correlated with impact variables. The cracking type and severity was reasonably predicted using finite element models. Mean stress affects were also studied on impact-damaged fatigue samples. The fatigue strength was accurately predicted based on the flaw size using a threshold-based, fracture mechanics approach. To study the effects of wear due to potential applications in a blade-disk dovetail arrangement, the machined Ti-47-2-2 alloy was fretted against In-718 using pin-on-disk experiments. Wear mechanisms were documented and compared to those of Ti-6Al-4V. A few fatigue samples were also fretted and subsequently fatigue tested. It was found that under the conditions studied, the fretting was not severe enough to affect the fatigue strength of g-TiAl.

Draper, Susan L.↗

NASA NTRS1985

Grain refinement of high strength steels to improve cryogenic toughness

Grain-refining techniques using multistep heat treatments to reduce the grain size of five commercial high-strength steels were investigated. The goal of this investigation was to improve the low-temperature toughness as measured by Charpy V-notch impact test without a significant loss in tensile strength. The grain size of four of five alloys investigated was successfully reduced up to 1/10 of original size or smaller with increases in Charpy impact energy of 50 to 180 percent at -320 F. Tensile properties were reduced from 0 to 25 percent for the various alloys tested. An unexpected but highly beneficial side effect from grain refining was improved machinability.

Rush, H. F.↗

NASA NTRS2024

Additively Manufactured Plastic Plasma Spectrometer

We report results in development and testing of a low resource top hat electrostatic analyzer (ESA) for space plasma measurements. This device has been additively manufactured (3D-printed) using fused deposition modeling. The classic top hat design is composed of four plastic pieces, without any surface coatings. The three conducting electrodes are printed from carbon nanotube infused polyether ether ketone (CNT-PEEK). The fourth piece, an insulating electrode support, uses pure PEEK. This ESA is designed to detect electrons in space from 10eV up to 30 keV. We demonstrate that the printed CNT-PEEK is sufficiently electrically conductive to support the fast high voltage slewing often required for high time resolution measurements. The plastic ESA has been successfully vibrated beyond standard pre-flight levels, tested under keV electron beam illumination over a wide range of temperatures, and tested under UV illumination, simulating the solar Ly-α flux. In comparison with an identical machined aluminum ESA, our CNT-PEEK ESA provides nominal energy/angle band passes, closely matching simulation. These band passes imply minimal impact from surface charging at beam energies of 2-3 keV, although more investigation is needed. We also find that the CNT-PEEK ESA provides far superior out-of-band electron rejection and UV photon rejection compared to the machined aluminum ESA. We do not detect any problems with trapped gases or outgassing. This development offers potential for significant mass savings, implementation of otherwise unattainable geometric configurations, and dramatic simplification in manufacturing and assembly processes required for development of space plasma instruments.

Quetzal Larrick↗

NASA NTRS2004

Characterization of Composite Fan Case Resins

The majority of commercial turbine engines that power today s aircraft use a large fan driven by the engine core to generate thrust which dramatically increases the engine s efficiency. However, if one of these fan blades fails during flight, it becomes high energy shrapnel, potentially impacting the engine or puncturing the aircraft itself and thus risking the lives of passengers. To solve this problem, the fan case must be capable of containing a fan blade should it break off during flight. Currently, all commercial fan cases are made of either just a thick metal barrier or a thinner metal wall surrounded by Kevlar-an ultra strong fiber that elastically catches the blade. My summer 2004 project was to characterize the resins for a composite fan case that will be lighter and more efficient than the current metal. The composite fan case is created by braiding carbon fibers and injecting a polymer resin into the braid. The resin holds the fibers together, so at first using the strongest polymer appears to logically lead to the strongest fan case. Unfortunately, the stronger polymers are too viscous when melted. This makes the manufacturing process more difficult because the polymer does not flow as freely through the braid, and the final product is less dense. With all of this in mind, it is important to remember that the strength of the polymer is still imperative; the case must still contain blades with high impact energy. The research identified which polymer had the right balance of properties, including ease of fabrication, toughness, and ability to transfer the load to the carbon fibers. Resin deformation was studied to better understand the composite response during high speed impact. My role in this research was the testing of polymers using dynamic mechanical analysis and tensile, compression, and torsion testing. Dynamic mechanical analysis examines the response of materials under cyclic loading. Two techniques were used for dynamic mechanical analysis. The ARES Instrument analyzed the material through torsion. The second machine, TA Instruments apparatus, applied a bending force to the specimen. These experiments were used to explore the effects of temperature and strain rate on the stiffness and strength of the resins. The two different types of loading allowed us to verify our results. An axial-torsional load frame, manufactured by MTS Systems, Inc., was used to conduct the tensile, compression, and torsional testing. These tests were used to determine the stress-strain curves for the resins. The elastic and plastic deformation data was provided to another team member for characterization of high fidelity material property predictions. This information was useful in having a better understanding of the polymers so that the fan cases could be as sturdy as possible. Deformation studies are the foundation for the computational modeling that provides the structural design of a composite engine case as well as detailed analysis of the blade impact event.

Dvoracek, Charlene M.↗

NASA NTRS1992

Manufacture and performance of carbon/epoxy 3-D woven composites

This paper evaluates 3-D orthogonal woven carbon/epoxy composites. Preforms were manufactured on an automatic 3-D weaving machine developed at N.C. State University. Matrix infiltration was conducted at MBB Central Laboratories. Testing was carried out at both locations and the joint results will be reported. The properties investigated include: interlaminar shear strength, compression, compression after impact, bending, tensile and penetration resistance. The 3-D orthogonal woven composites were compared with laminated and other 3-D composites made with preforms having interlock structure. C-scans were used to examine the quality of infiltration and the damage area after impact. The performance of the composites made from the 3-D orthogonal preforms showed superior properties compared to the other composites. The penetration resistance test showed unexpectedly very good performance.

Brandt, J.↗

NASA NTRS1997

Compression Response of a Sandwich Fuselage Keel Panel With and Without Damage

Results are presented from an experimental and analytical study of a sandwich fuselage keel panel with and without damage. The fuselage keel panel is constructed of graphite-epoxy skins bonded to a honeycomb core, and is representative of a highly loaded fuselage keel structure. The face sheets of the panel contain several terminated or dropped plies along the length of the panel. The results presented provide a better understanding of the load distribution in damaged and undamaged thick-face-sheet composite sandwich structure with dropped plies and of the failure mechanisms of such structure in the presence of low-speed impact damage and discrete-source damage. The impact-damage condition studied corresponds to barely visible impact damage (BVID), and the discrete-source damage condition studied is a notch machined through both face sheets. Results are presented from an impact-damage screening study conducted on another panel of the same design to determine the impact energy necessary to inflict BVID on the panel. Results are presented from compression tests of the panel in three conditions: undamaged; BVID in two locations; and BVID in two locations and a notch through both face sheets. Surface strains in the face sheets of the undamaged panel and the notched panel obtained experimentally are compared with finite element analysis results. The experimental and analytical results suggest that for the damage conditions studied, discrete-source damage influences the structural performance more than BVID.

McGowan, David M.↗

NASA NTRSDate not supplied

A Historical Overview of the NASA Orbital Debris Program Office’s Laboratory Optical Measurements

The NASA Orbital Debris Program Office (ODPO) has used laboratory measurements to help bring ground-based measurements together with models to ascertain Earth-orbiting target parameters of interest to support various orbital debris models. In 2005, the Optical Measurement Center (OMC) was established to simulate space-based illumination conditions using equipment and techniques that recreate telescopic observations, particularly source-target-sensor orientations. The intent was to recreate light curves using known aspect angles of known targets and phase angles (angle is defined by the vertex between illumination source-object-detector) to complement telescopic observations that could be used to update the current optical size estimation model (OSEM) – a model that converts object brightness into size for orbital debris models. To support the above goals, the laboratory has undergone several equipment upgrades to increase capabilities over almost 20 years of operation. The primary instrumentation acquires reflectance measurements and includes a solar-like light source, CCD camera with astrometric filters, and robotic arm. A rotary arm was added approximately five years after full operation to allow acquisition through a full 360° range of phase angles. Another part of the OMC instrumentation is a field spectrometer, predominately used for field operations to acquire pre- and post-flight spacecraft material spectral measurements. Additionally, reflectance spectroscopy of various materials is also of interest resulting from hypervelocity impact tests, pristine spacecraft materials, or samples of materials that are used in spacecraft design. These measurements are stored in NASA’s Spectral Material Database, a resource that is still being populated today. The study of spectral measurements also enabled the development of spectral unmixing routines to support the identification of spacecraft materials from spectral data gathered by ground based telescopes. Preliminary OMC investigations focused on feasibility studies to acquire 360° rotation light curves of simple shapes at a single-phase angle and extended to measurements of representative fragments from ground-based explosion tests. To correlate the light curves with ground-based optical measurements, a focused study on high area to mass materials was conducted in support of a newly identified population (at the time) in geosynchronous orbit (GEO) consisting of multi-layered insulation. To further characterize orbital debris, a larger selection of materials was analyzed using laboratory photometric measurements that included representative targets from pristine spacecraft materials and ground-based impact tests. Around 2012, an initiative was requested to understand the feasibility of active debris removal (ADR) of larger targets using grappling methods for spent rocket bodies. Using a priori information on selected targets, scaled-down versions of rocket bodies were generated thanks to improvements in 3D printing technology and machining. These targets were studied in the OMC to understand rotation characteristics. These were compared with telescopic data to determine if the tumble and rotation angles would allow ADR. In 2013, the OMC focused on combining spectral measurements with photometric data to characterize GEO orbital debris. Several years later, NASA acquired a Titan III Transtage test article from “The Boneyard” with a high-resemblance to on-orbit Titan III Transtage rocket bodies, allowing physical access to a representative rocket body that suffered fragmentations in GEO. This prompted the creation of 3D models using lidar technology and spectral measurements of the materials. Focused research also transitioned to specific materials (i.e., solar cells) when telescopic surveys requested characterization of specific GEO targets. In the different research products presented, the focus has been to understand the various parameters that influence optical size estimation, including albedo, phase functions, and brightness variations. Work in this area continues with newer sources of data, including DebriSat, a high-fidelity 56-kg spacecraft replica representative of a modern low Earth orbit (LEO) satellite subjected to a laboratory hypervelocity impact test to understand fragmentation events and to support updates to satellite breakup models and size estimation models. Utilizing the vast population of fragments from DebriSat and prior laboratory impact experiments, the ODPO has focused on acquiring bidirectional reflectance distribution function (BRDF) data to characterize targets in the laboratory, thus removing aspect angle dependencies. Additionally, the DebriSat project has provided improved processes for measuring size via image acquisition, such that a true fragment size can be directly compared to the derived size using the OSEM. The team continues to assess BRDFs and use spectral measurement data to investigate the parameters used in the OSEM, specifically magnitudes, albedo, and phase functions.

Heather Cowardin↗

NASA NTRS2017

Computed Tomography Inspection and Analysis for Additive Manufacturing Components

Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws and geometric features were inspected using a 2-megavolt linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed to determine the impact of additive manufacturing on inspectability of objects with complex geometries.

Beshears, Ronald D.↗

NASA NTRS2016

Computed Tomography Inspection and Analysis for Additive Manufacturing Components

Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws were inspected using a 2MeV linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed using standard image analysis techniques to determine the impact of additive manufacturing on inspectability of objects with complex geometries.

Beshears, Ronald D.↗

NASA NTRS2016

Additive Manufacturing, Design, Testing, and Fabrication: A Full Engineering Experience at JSC

I worked on several projects this term. While most projects involved additive manufacturing, I was also involved with two design projects, two testing projects, and a fabrication project. The primary mentor for these was Richard Hagen. Secondary mentors were Hai Nguyen, Khadijah Shariff, and fabrication training from James Brown. Overall, my experience at JSC has been successful and what I have learned will continue to help me in my engineering education and profession long after I leave. My 3D printing projects ranged from less than a 1 cubic centimeter to about 1 cubic foot and involved several printers using different printing technologies. It was exciting to become familiar with printing technologies such as industrial grade FDM (Fused Deposition Modeling), the relatively new SLA (Stereolithography), and PolyJet. My primary duty with the FDM printers was to model parts that came in from various sources to print effectively and efficiently. Using methods my mentor taught me and the Stratasys Insight software, I was able to minimize imperfections, hasten build time, improve strength for specific forces (tensile, shear, etc...), and reduce likelihood of a print-failure. Also using FDM, I learned how to repair a part after it was printed. This is done by using a special kind of glue that chemically melts the two faces of plastic parts together to form a fused interface. My first goal with SLA technology was to bring the printer back to operational readiness. In becoming familiar with the Pegasus SLA printer, I researched the leveling, laser settings, and different vats to hold liquid material. With this research, I was successfully able to bring the Pegasus back online and have successfully printed multiple sample parts as well as functional parts. My experience with PolyJet technology has been focused on an understanding of the abilities/limits, costs, and the maintenance for daily use. Still upcoming will be experience with using a composite printer that uses FDM technology to print plastic while laying an internal filament of Kevlar or carbon-fiber inside the printed material. It has been incredible being exposed to this range of technologies and I feel very fortunate to be ready for virtually any kind of printing technology I come across in the future. Design work played a part in my internship this term as well. Working with Hai Nguyen, I was able to design a set of testing tips and a test frame for use with an Arcjet. The testing tips will be made of several different materials that will possibly be used in the heat shield of the Orion space craft. These designs included technical drawings that were presented to the fabrication shop. The frame design was created from 80/20 (a popular brand of frame construction equipment) and included an order form with pricing for fabrication. An independent design was also done for the virtual reality lab. This design was to create a hand-controller based on a previous design. This final design was sent directly to a 3D printer without technical drawings. Overall, my design work has given me experience with using 80/20, helped improve my CAD (Computer Aided Design) proficiency, and increased my knowledge of how to set up technical drawings for fabrication. The final role I have played in this internship has been to assist with testing of the inflatable technology materials working Khadijah Shariff. I began the internship assisting with permeability testing with the initial plan to continue the testing independently after training. Unfortunately, the testing apparatus suffered a technical failure and had funding pulled which cancelled that portion of the project. Further testing with inflatable technology continued with tensile testing of various stitching methods. This testing took place over a two-days and concluded successfully. Final testing was to be more tensile testing but of straps used to connect various inflatable sections. Unfortunately, the needed grips for the tensile tests could not be located and put the testing on hold. It is possible this round of testing will still take place by the end of the internship if the grips can be found. Overall, this portion of the internship has helped me become familiar with one kind of permeability test as well as a popular tensile/compression testing machine. Finally, I also had the chance to be trained using a metal lathe for making very small tips for a soldering iron. These tips will be used to melt threaded brass inserts into 3D printed plastic. Working with James Brown, I was able to successfully machine a brass rod down to as little as 0.064 inches plus or minus 0.001 inches. It was very rewarding to learn how to best use the machine and become familiar with a skill that will undoubtedly be used again in the future. I have been told by several professional engineers that learning to use a lathe and mill will be invaluable skills in the field. This knocks 50 percent of that goal off and I look forward to learning the mill at some point in the future. As is apparent with this list of projects, my internship was not focused on a single over-arching goal. Instead, I was able to gain experience in a myriad of very different areas. I feel like my time here was spent bouncing from one project to the next. Though sometimes difficult to switch gears, it was very rewarding to be a part of so much in so little time. My career and education will be positively impacted by what I have learned at JSC. My experience with 3D printing has improved my ability to handle many issues that may come up in the future with multiple different technologies. The design work I took part in, especially creating technical drawings, will help me better present designs to any engineer or shop I will encounter. My testing experience has helped me become familiar with a popular kind of tensile test machine that will likely be similar to the kinds I will encounter in the future. Finally, my experience with fabrication has given me a rare opportunity, as an engineer, to take part in the fabrication of a part. This experience will help me better tailor my future designs for the manufacturing process and has given me an appreciation for detailed/delicate machining work. My experience at JSC has been successful and will continue to assist me for a long time within the engineering field.

Zusack, Steven↗

NASA NTRS2008

Compression After Impact Testing of Sandwich Structures Using the Four Point Bend Test

For many composite laminated structures, the design is driven by data obtained from Compression after Impact (CAI) testing. There currently is no standard for CAI testing of sandwich structures although there is one for solid laminates of a certain thickness and lay-up configuration. Most sandwich CAI testing has followed the basic technique of this standard where the loaded ends are precision machined and placed between two platens and compressed until failure. If little or no damage is present during the compression tests, the loaded ends may need to be potted to prevent end brooming. By putting a sandwich beam in a four point bend configuration, the region between the inner supports is put under a compressive load and a sandwich laminate with damage can be tested in this manner without the need for precision machining. Also, specimens with no damage can be taken to failure so direct comparisons between damaged and undamaged strength can be made. Data is presented that demonstrates the four point bend CAI test and is compared with end loaded compression tests of the same sandwich structure.

Nettles, Alan T.↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

Saik Anam Siam↗

NASA NTRSDate not supplied

Multiscale Modeling of Woven Ablative Thermal Protection System Materials

The NASA Entry Systems Modeling project maintains a portfolio of computational model and tool development activities focused on reducing performance uncertainties in ablative Thermal Protection System (TPS) materials for NASA missions. The development activities span material scale and strive to allow microstructural characterization of material structure and properties, mesoscale analyses of damage, and macroscale evaluation of heatshield performance and recession in a given aerothermodynamic environment. This talk will detail the application of developed capabilities at all three scales to the woven TPS material that the Agency has selected as the heatshield for the Mars Sample Return Earth Entry System (MSR-EES) mission – 3D Mid-Density Carbon Phenolic (3MDCP). Each of the applications focuses on driving down uncertainties in material performance and thus risk for MSR-EES and other future missions that may leverage woven TPS. At the microscale, machine learning techniques are used to characterize images from destructive microscopy and inform structural variability. At the mesoscale, Lagrangian techniques are used to simulate ballistic impact and interpret damage modes noted in experiments. At the macroscale, coupled flow-material response techniques are validated by Arc Jet testing to enable heatshield design for missions with massive ablation.

Justin B Haskins↗

NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗

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 NTRS1982

Utility experience with two demonstration wind turbine generators

Edison has committed 360 MW of nameplate generating capacity to wind energy by year 1990 in its long-range generation plan. To reach this goal the Company's wind energy program focuses on three areas: the continuous evaluation of the wind resource, the hands-on demonstration of wind turbine generators (WTG) and an association with wind park developers. Two demonstration WTGs have been installed and operated at Edison's Wind Energy Center near Palm Springs, California: a 3 MW horizontal axis Bendix/Schachle WTG and a 500 kW vertical axis Alcoa WTG. They are part of a one to two year test program during which the performance of the WTGs will be evaluated, their system operation and environmental impact will be assessed and the design criteria of future WTGs will be identified. Edison's experience with these two WTGs is summarized and the problems encountered with the operation of the two machines are discussed.

Wehrey, M. C.↗