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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Specification and preliminary design of an array processor

The design of a computer suited to the class of problems typified by the general circulation of the atmosphere was investigated. A fundamental goal was that the resulting machine should have roughly 100 times the computing capability of an IBM 360/95 computer. A second requirement was that the machine should be programmable in a higher level language similar to FORTRAN. Moreover, the new machine would have to be compatible with the IBM 360/95 since the IBM machine would continue to be used for pre- and post-processing. A third constraint was that the cost of the new machine was to be significantly less than that of other extant machines of similar computing capability, such as the ILLIAC IV and CDC STAR. A final constraint was that it should be feasible to fabricate a complete system and put it in operation by early 1978. Although these objectives were generally met, considerable work remains to be done on the routing system.

Slotnick, D. L.↗
NASA NTRS2002

Software for Partly Automated Recognition of Targets

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user's tendencies while the user is selecting targets and to increase the user's productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Opitz, David↗
NASA NTRS2008

Electron Beam Freeform Fabrication: A Fabrication Process that Revolutionizes Aircraft Structural Designs and Spacecraft Supportability

The technological inception and challenges, as well as current applications of the electron beam freeform fabrication (EBF3) process are outlined. The process was motivated by the need for a new metals technology that would be cost-effective, enable the production of new alloys and that would could be used for efficient, lightweight structures. EBF3 is a rapid metal fabrication, layer-additive process that uses no molds or tools and which yields properties equivalent to wrought. The benefits of EBF3 include it near-net shape which minimizes scrap and reduces part count; efficiency in design which allows for lighter weight and enhanced performance; and, its "green" manufacturing process which yields minimal waste products. EBF3 also has a high tensile strength, while a structural test comparison found that EBF3 panels performed 5% lower than machined panels. Technical challenges in the EBF3 process include a need for process control monitoring and an improvement in localized heat response. Currently, the EBF3 process can be used to add details onto forgings and to construct and form complex shapes. However, it has potential uses in a variety of industries including aerospace, automotive, sporting goods and medical implant devices. The novel structural design capabilities of EBF3 have the ability to yield curved stiffeners which may be optimized for performance, low weight, low noise and damage tolerance applications. EBF3 has also demonstrated its usefulness in 0-gravity environments for supportability in space applications.

Taminger, Karen M.↗
NASA NTRS2003

Software for Partly Automated Recognition of Targets

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user s tendencies while the user is selecting targets and to increase the user s productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Opitz, David↗
NASA NTRS2011

MaROS: Information Management Service

This software is provided by the Mars Relay Operations Service (MaROS) task to a variety of Mars projects for the purpose of coordinating communications sessions between landed spacecraft assets and orbiting spacecraft assets at Mars. The Information Management Service centralizes a set of functions previously distributed across multiple spacecraft operations teams, and as such, greatly improves visibility into the end-to-end strategic coordination process. Most of the process revolves around the scheduling of communications sessions between the spacecraft during periods of time when a landed asset on Mars is geometrically visible by an orbiting spacecraft. These relay sessions are used to transfer data both to and from the landed asset via the orbiting asset on behalf of Earth-based spacecraft operators. This software component is an application process running as a Java virtual machine. The component provides all service interfaces via a Representational State Transfer (REST) protocol over https to external clients. There are two general interaction modes with the service: upload and download of data. For data upload, the service must execute logic specific to the upload data type and trigger any applicable calculations including pass delivery latencies and overflight conflicts. For data download, the software must retrieve and correlate requested information and deliver to the requesting client. The provision of this service enables several key advancements over legacy processes and systems. For one, this service represents the first time that end-to-end relay information is correlated into a single shared repository. The software also provides the first multimission latency calculator; previous latency calculations had been performed on a mission-by-mission basis.

Allard, Daniel A.↗
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 NTRS1992

Execution models for mapping programs onto distributed memory parallel computers

The problem of exploiting the parallelism available in a program to efficiently employ the resources of the target machine is addressed. The problem is discussed in the context of building a mapping compiler for a distributed memory parallel machine. The paper describes using execution models to drive the process of mapping a program in the most efficient way onto a particular machine. Through analysis of the execution models for several mapping techniques for one class of programs, we show that the selection of the best technique for a particular program instance can make a significant difference in performance. On the other hand, the results of benchmarks from an implementation of a mapping compiler show that our execution models are accurate enough to select the best mapping technique for a given program.

Sussman, Alan↗
NASA NTRS2011

Computer-Aided Parallelizer and Optimizer

The Computer-Aided Parallelizer and Optimizer (CAPO) automates the insertion of compiler directives (see figure) to facilitate parallel processing on Shared Memory Parallel (SMP) machines. While CAPO currently is integrated seamlessly into CAPTools (developed at the University of Greenwich, now marketed as ParaWise), CAPO was independently developed at Ames Research Center as one of the components for the Legacy Code Modernization (LCM) project. The current version takes serial FORTRAN programs, performs interprocedural data dependence analysis, and generates OpenMP directives. Due to the widely supported OpenMP standard, the generated OpenMP codes have the potential to run on a wide range of SMP machines. CAPO relies on accurate interprocedural data dependence information currently provided by CAPTools. Compiler directives are generated through identification of parallel loops in the outermost level, construction of parallel regions around parallel loops and optimization of parallel regions, and insertion of directives with automatic identification of private, reduction, induction, and shared variables. Attempts also have been made to identify potential pipeline parallelism (implemented with point-to-point synchronization). Although directives are generated automatically, user interaction with the tool is still important for producing good parallel codes. A comprehensive graphical user interface is included for users to interact with the parallelization process.

Jin, Haoqiang↗
NASA NTRSDate not supplied

Extreme Environment Hot Fire Durability of Post-Processed Additively Manufactured GRCop-Alloy Combustion Chambers in LOX/Hydrogen and LOX/Methane

Extreme environment survivability of metal additive manufactured (AM) GRCop-alloy thrust chambers has been demonstrated in different bi-propellants at near stoichiometric and even oxygen rich combustion. GRCop-alloy chambers tested at NASA Marshall Space Flight Center (MSFC) have accumulated over 26,000 seconds of hot fire duration and over 500 starts. These chambers are produced using an AM process called laser powder bed fusion (L-PBF). A major feature of this process is high wall surface roughness which can be customized in post processing to leverage various performance advantages. Post-processing can include heat treatment, final machining, polishing, and welding and is key to hardware survivability in extreme environments. Surface finish enhancement techniques were applied to the hot wall and coolant channels to reduce the overall total heat load to the chamber walls. Performance optimization of various thrust class TCA’s is a strategic technology goal of NASA MSFC. Three different chamber geometries using cryogenic methane and de-ionized water as coolants were hot fire tested to obtain their life cycle, pressure drop, and heat load performances. Several 1.2K lbf LOX/H2 chambers, 1K lbf LOX/CH4 chambers, and 7K lbf LOX/CH4 chambers were tested. All post-processed configurations performed remarkably well when subjected to extreme hot fire test conditions. Streaking and blanching due to localized oxygen rich conditions were observed on some test articles in their as-built surface finish state. However, this is actually a function of the injector mixing and only serves to further establish the durability of L-PBF GRCop-alloy thrust chambers. Several different polishing techniques were applied to the hot wall and integrated coolant channels prior to hot fire testing and their performances assessed. Overall, AM produced GRCop chambers are extremely reliable, durable, and customizable to the desired performance metrics.

grcop↗
NASA NTRS1974

Finding the earth in ERTS

Itek's Optical System Division recently completed an investigation funded by NASA to develop interpretation methods and algorithms suitable for recognition of earth resources by machines using multispectral data from ERTS. Through the algorithms developed (and described here) it is now possible to automatically recognize terrain types. The clustering algorithm guarantees high accuracy in the recognition process with almost complete automation. Interestingly, the machine recognition seems to be more accurate than a human photointerpreter who has been restricted to using only ERTS-1 color composites. That is, machine recognition appears to be more sensitive, it can operate much closer, to the resolution limit of the ERTS-1 imagery than the human photointerpreter.

Gramenopoulos, N.↗
NASA NTRS2002

Friction-Stir Welding of Aluminum For the Space Program

The Marshall Space Flight Center is developing and characterizing the friction stir welding process for the Space Shuttle and other space programs. This revolutionary process, invented and patented by The Weld Institute in England, offers tremendous advantages for joining aluminum for high performance applications. It is particularly suited for advanced aluminum-lithium alloys, such as 2195, the primary structural alloy used in the External Tank. The friction stir welding process joins metals with minimal heat input, resulting in high-strength joints with high ductility. It is a simple process to demonstrate using a common milling machine for sample parts, but relatively expensive to implement on large-scale hardware, due to the high cost of tooling needed to handle the high forging pressures characteristic of the process. Recent developments at the Marshall Space Flight Center have demonstrated friction stir welding on linear joints up to 5 meters (15 ft.), with material thickness ranging between 2.5 mm and 16.5 mm (0.100" to 0.650"). High efficiency weld joints have been produced in aluminum from the 2000, 5000, and 6000 series alloy systems. A "retractable pin tool" system was patented by MSFC that allows use of friction stir welding for joints with changing material thickness, and with less rigid tooling than previously considered. This presentation will describe the details of alloys welded to-date and technical advances under development at MSFC. These developments could have substantial benefit to industrial applications for welding aluminum.

Jones, Clyde S.↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRS1988

Two dimensional convolute integers for machine vision and image recognition

Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

Edwards, Thomas R.↗
NASA NTRS1989

PRIME: A bottom-up approach to probabilistic rule development

PRIME is a system to be used by an intelligent machine to allow it to operate in an abstract but uncertain (or stochastic) environment. It maintains a model of the effects of the machine's actions in the form of a rule base, which is induced from experience. This bottom-up approach to rule development allows the model to adapt to changes in the environment. Each rule consists of a condition under which the rule is active, an action, the effect of the action on the environment, and an estimate of the probability of this effect occurring. The effect probabilities are used to model the uncertainty in the environment, permitting multiple possible effects for a single action under a particular set of conditions. The objective of the intelligent machine is to satisfy user-specified goals with maximum probability of success. PRIME fulfills this requirement in two ways: it continuously updates the rule base with the most recent information, to ensure the validity of the model; and it generates plans which have the maximum probability of achieving the goals, based on the probability estimates in the rule base. PRIME is composed of three main processes: exploration, generalization, and planning. In exploration, the machine executes various randomly chosen actions, observes the effects on the environment, and updates the rule base accordingly. This process is used to develop the rule base in simulation, as well as to supplement the current knowledge during normal operation. Generalization is the procedure used to induce general rules from experience, which is encoded in the form of specific rules. These general rules extend the machine's knowledge to situations which have not been encountered yet, thereby increasing the capability of the machine to plan effectively. Planning is the process of constructing an optimal sequence of actions to satisfy a goal, using the rule base to predict the effects of these actions and to determine the probability of success of the plan. The rule representation and many other data structures were specifically chosen to maximize the efficiency of these processes. A simulated environment was designed to test the performance of PRIME. The results of experimentation were largely negative. The main problem was that the domain coverage of the rules was inadequate for the number of rules stored in the rule base, due to redundancies in general rules and numerous rules covering ineffective actions. It was determined that a more efficient generalization, and some form of goal-directed exploration, are necessary in order to solve most of the current deficiencies in PRIME.

Miller, Scott A.↗
NASA NTRS2022

Development of Advanced Manufacturing Approaches for Single-Piece Launch Vehicle Structures

Advanced, near-net shape manufacturing methods have the potential to enable production of structures with fewer welds and reduced machining requirements. Two such methods that are suitable for the manufacturing of single-piece, stiffened barrel-shaped structures are presented. The first solution deployed existing manufacturing technology to produce a thick-walled barrel with integrally machined stiffeners. In this study, an 8-ft. diameter barrel was produced and subjected to a buckling test. Data on the manufacturing and testing of this barrel are provided and compared against the traditional multi-piece weld construction approach. The single-piece barrel resulted in a 28% greater load carrying capacity than the welded barrel. A second solution utilized a novel flow-forming technique to produce the barrel and stiffeners in one process without the need for welding or machining. This is an emerging manufacturing method known as the Integrally Stiffened Cylinder (ISC) process. This innovative process is being evaluated for launch vehicle and commercial aircraft manufacturing. The one-piece, stiffened ISC barrels, which have been successfully fabricated at 10 ft. in diameter, offer a direct replacement for conventional multi-piece, welded or riveted structures. A cost-benefit analysis for launch vehicle cryogenic propellant tanks estimated that the ISC process offers up to a 50% reduction in manufacturing costs and a 10% reduction in mass.

integrally stiffened cylinder (ISC)↗
NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗