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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Advanced tow placement of composite fuselage structure

The Hercules NASA ACT program was established to demonstrate and validate the low cost potential of the automated tow placement process for fabrication of aircraft primary structures. The program is currently being conducted as a cooperative program in collaboration with the Boeing ATCAS Program. The Hercules advanced tow placement process has been in development since 1982 and was developed specifically for composite aircraft structures. The second generation machine, now in operation at Hercules, is a production-ready machine that uses a low cost prepreg tow material form to produce structures with laminate properties equivalent to prepreg tape layup. Current program activities are focused on demonstration of the automated tow placement process for fabrication of subsonic transport aircraft fuselage crown quadrants. We are working with Boeing Commercial Aircraft and Douglas Aircraft during this phase of the program. The Douglas demonstration panels has co-cured skin/stringers, and the Boeing demonstration panel is an intricately bonded part with co-cured skin/stringers and co-bonded frames. Other aircraft structures that were evaluated for the automated tow placement process include engine nacelle components, fuselage pressure bulkheads, and fuselage tail cones. Because of the cylindrical shape of these structures, multiple parts can be fabricated on one two placement tool, thus reducing the cost per pound of the finished part.

Anderson, Robert L.↗
NASA NTRS2001

Cryogenic Treatment of Al-Li Alloys for Improved Weldability, Repairability, and Reduction of Residual Stresses

Service performance has shown that cryogenic treatment of some metals provides improved strength, fatigue life, and wear resistance to the processed material. Effects such as these were initially discovered by NASA engineers while evaluating spacecraft that had returned from the cold vacuum of space. Factors such as high cost, poor repairability, and poor machinability are currently prohibitive for wide range use of some aerospace aluminum alloys. Application of a cryogenic treatment process to these alloys is expected provide improvements in weldability and weld properties coupled with a reduction in repairs resulting in a significant reduction in the cost to manufacture and life cycle cost of aerospace hardware. The primary purpose of this effort was to evaluate the effects of deep cryogenic treatment of some aluminum alloy plate products, welds, and weld repairs, and optimize a process for the treatment of these materials. The optimized process is being evaluated for improvements in properties of plate and welds, improvements in weldability and repairability of treated materials, and as an alternative technique for the reduction of residual stresses in repaired welds. This paper will present the results of testing and evaluation conducted in this effort. These results will include assessments of changes in strength, toughness, stress corrosion susceptability, weldability, repairability, and reduction in residual stresses of repaired welds.

Malone, Tina W.↗
NASA NTRS1986

Fundamentals and advances in the development of remote welding fabrication systems

Operational and man-machine issues for welding underwater, in outer space, and at other remote sites are investigated, and recent process developments are described. Probable remote welding missions are classified, and the essential characteristics of fundamental remote welding tasks are analyzed. Various possible operational modes for remote welding fabrication are identified, and appropriate roles for humans and machines are suggested. Human operator performance in remote welding fabrication tasks is discussed, and recent advances in the development of remote welding systems are described, including packaged welding systems, stud welding systems, remotely operated welding systems, and vision-aided remote robotic welding and autonomous welding systems.

Agapakis, J. E.↗
NASA NTRSDate not supplied

Demonstration of How Manufacturing Innovations Challenge Conventional Structural Design

For almost 100 years, commercial aircraft have been fabricated using riveted aluminum alloy structures. Aside from refining aircraft designs for better aerodynamic efficiency, improving alloy compositions, and automating assembly steps, fuselage construction remains largely unchanged today. The intent of the lightweight metallic fuselage prototype undertaken in NASA’s Advanced Air Transport Technology project was to demonstrate innovative forming and joining processes that advance the design paradigm, i.e. achieve high-rate manufacturing and reduce assembly time/costs. Over the past decade, researchers at NASA Langley Research Center have evaluated the potential to modify the flow forming process to produce near-net shaped cylinders, integrally stiffened along the cylinder axis, for space launch vehicles. The production of integral stiffeners in a formed aluminum cylinder, the size of the Space Shuttle external tank, replaced machined thick plate and welding steps to eliminate > 500,000 pounds of machining chips and ~ 0.5 miles of welds. The flow forming method, called the Integrally Stiffened Cylinder (ISC) process, was successfully demonstrated up to the 10-foot diameter scale, which laid the groundwork for the current investigation of metallic fuselage structures. Using the ISC process as the basis for the re-design of a metallic fuselage eliminates hundreds of thousands of holes and rivets, while significantly reducing assembly time and crack initiation sites in the integral structure. However, the ISC process is not currently configured to fabricate circumferential ring frames for carrying fuselage internal pressure loads. Consequently, a trade study was performed to assess existing and advanced manufacturing processes, including additive manufacturing, forming, and welding. The approach with the lowest barriers to success, while simultaneously improving manufacturing time and cost, was to use formed ring frame segments attached to the ISC via Refill Friction Stir Spot Welding (RFSSW). The RFSSW process is five times faster than drilling, reaming, and riveting and provides similar mechanical performance. Finishing the fuselage structure with conventional windows, floor beams, and floor panels can then be accomplished using incumbent assembly methods, thereby maximizing reuse of existing infrastructure for aircraft construction. Structural analyses were performed to assess and optimize the geometric variables of integrated skin and stiffener configurations. A cost benefit and manufacturing rate analysis was also performed to compare against the current state-of-the-art for single aisle transport class aircraft. The resulting structure offers a weight reduction that rivals current graphite-epoxy composite fuselage structures. The projected manufacturing rate is close to double current metallic fuselages and six times faster than current composite manufacturing practices. The damage tolerance properties of a monocoque fuselage structure have yet to be assessed, but past integral airframe structural work has exploited geometric features to blunt or turn cracks. This concept offers promise that integrated structures can meet stringent aircraft durability specifications. An important benefit is that such aluminum fuselage structures may be inspected and repaired using established practices and existing expertise. Finally, pursuit of advanced manufacturing processes for future aluminum fuselages minimizes waste and the structure is 100% recyclable at the end-of-life for maximum sustainability.

aluminum↗
NASA NTRS1989

Worldnet

The expanding use of powerful workstations coupled to ubiquitous networks is transforming scientific and engineering research and the the ways organizations around the world do business. By the year 2000, few enterprises will be able to succeed without mastery of this technology, which will be embodied in an information infrastructure based on a worldwide network. A recurring theme in all the discussions of what might be possible within the emerging Worldnet is people and machines working together in new ways across distance and time. A review is presented of the basic concepts on which the architecture of Worldnet must be built: coordination of action, authentication, privacy, and naming. Worldnet must provide additional functions to support the ongoing processes of suppliers and consumers: help services, aids for designing and producing subsystems, spinning off new machines, and resistance to attack. This discussion begins to reveal the constituent elements of a theory for Worldnet, a theory focused on what people will do with computers rather than on what computers do.

Denning, Peter J.↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗
NASA NTRS2002

The Efficiency and the Scalability of an Explicit Operator on an IBM POWER4 System

We present an evaluation of the efficiency and the scalability of an explicit CFD operator on an IBM POWER4 system. The POWER4 architecture exhibits a common trend in HPC architectures: boosting CPU processing power by increasing the number of functional units, while hiding the latency of memory access by increasing the depth of the memory hierarchy. The overall machine performance depends on the ability of the caches-buses-fabric-memory to feed the functional units with the data to be processed. In this study we evaluate the efficiency and scalability of one explicit CFD operator on an IBM POWER4. This operator performs computations at the points of a Cartesian grid and involves a few dozen floating point numbers and on the order of 100 floating point operations per grid point. The computations in all grid points are independent. Specifically, we estimate the efficiency of the RHS operator (SP of NPB) on a single processor as the observed/peak performance ratio. Then we estimate the scalability of the operator on a single chip (2 CPUs), a single MCM (8 CPUs), 16 CPUs, and the whole machine (32 CPUs). Then we perform the same measurements for a chache-optimized version of the RHS operator. For our measurements we use the HPM (Hardware Performance Monitor) counters available on the POWER4. These counters allow us to analyze the obtained performance results.

Frumkin, Michael↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
NASA NTRS1978

Production technology for high efficiency ion implanted solar cells

Ion implantation is being developed for high volume automated production of silicon solar cells. An implanter designed for solar cell processing and able to properly implant up to 300 4-inch wafers per hour is now operational. A machine to implant 180 sq m/hr of solar cell material has been designed. Implanted silicon solar cells with efficiencies exceeding 16% AM1 are now being produced and higher efficiencies are expected. Ion implantation and transient processing by pulsed electron beams are being integrated with electrostatic bonding to accomplish a simple method for large scale, low cost production of high efficiency solar cell arrays.

Kirkpatrick, A. R.↗
NASA NTRS2003

Validation of On-board Cloud Cover Assessment Using EO-1

The purpose of this NASA Earth Science Technology Office funded effort was to flight validate an on-board cloud detection algorithm and to determine the performance that can be achieved with a Mongoose V flight computer. This validation was performed on the EO-1 satellite, which is operational, by uploading new flight code to perform the cloud detection. The algorithm was developed by MIT/Lincoln Lab and is based on the use of the Hyperion hyperspectral instrument using selected spectral bands from 0.4 to 2.5 microns. The Technology Readiness Level (TRL) of this technology at the beginning of the task was level 5 and was TRL 6 upon completion. In the final validation, an 8 second (0.75 Gbytes) Hyperion image was processed on-board and assessed for percentage cloud cover within 30 minutes. It was expected to take many hours and perhaps a day considering that the Mongoose V is only a 6-8 MIP machine in performance. To accomplish this test, the image taken had to have level 0 and level 1 processing performed on-board before the cloud algorithm was applied. For almost all of the ground test cases and all of the flight cases, the cloud assessment was within 5% of the correct value and in most cases within 1-2%.

Mandl, Dan↗
NASA NTRS2019

Utilizing Convolutional Neural Networks for Global Seagrass Habitat Mapping

Convolutional neural networks (CNNs) are becoming an increasingly prevalent machine learning algorithm due to their high accuracy and lack of reliance on heuristic processes. One of the major drawbacks of convolutional neural networks is their reliance on large amounts of training data in order to generate sensible results. This talk will cover how our team has utilized the strengths and overcome the weaknesses of convolutional neural networks as they apply to seagrass habitat mapping. We will share our technical CNN results over time, detail the requirements and challenges that our team overcame and explore how other teams can better incorporate a stronger seagrass component into their machine learning projects.

Convolutional↗
NASA NTRS1967

Isostatic compression process converts polyaromatics into structural material

Isostatic compression process compacts certain powdered aromatic polymers into homogeneous materials that can be machined to form useful components, such as bearings. It provides for complete removal of air in the interstitial spaces surrounding the granules of the powdered polymer before the powder is subjected to isostatic compression.

Ingham, J. D.↗
NASA NTRS1977

Reinforced Carbon Carbon (RCC) oxidation resistant material samples - Baseline coated, and baseline coated with tetraethyl orthosilicate (TEOS) impregnation

Reinforced carbon-carbon material specimens were machined from 19 and 33 ply flat panels which were fabricated and processed in accordance with the specifications and procedures accepted for the fabrication and processing of the leading edge structural subsystem (LESS) elements for the space shuttle orbiter. The specimens were then baseline coated and tetraethyl orthosilicate impregnated, as applicable, in accordance with the procedures and requirements of the appropriate LESS production specifications. Three heater bars were ATJ graphite silicon carbide coated with the Vought 'pack cementation' coating process, and three were stackpole grade 2020 graphite silicon carbide coated with the chemical vapor deposition process utilized by Vought in coating the LESS shell development program entry heater elements. Nondestructive test results are reported.

Gantz, E. E.↗
NASA NTRS1981

Testing panels in tension and flexure

Simple jig adapts tensile test machine for simulataneous application of tension and flexure, for evaluating panel composition, processing, and design. Environmental test chamber can be added so that panel properties can be measured at extreme temperatures.

Jing, G. K.↗