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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Parallel Simulation of Unsteady Turbulent Flames

Time-accurate simulation of turbulent flames in high Reynolds number flows is a challenging task since both fluid dynamics and combustion must be modeled accurately. To numerically simulate this phenomenon, very large computer resources (both time and memory) are required. Although current vector supercomputers are capable of providing adequate resources for simulations of this nature, the high cost and their limited availability, makes practical use of such machines less than satisfactory. At the same time, the explicit time integration algorithms used in unsteady flow simulations often possess a very high degree of parallelism, making them very amenable to efficient implementation on large-scale parallel computers. Under these circumstances, distributed memory parallel computers offer an excellent near-term solution for greatly increased computational speed and memory, at a cost that may render the unsteady simulations of the type discussed above more feasible and affordable.This paper discusses the study of unsteady turbulent flames using a simulation algorithm that is capable of retaining high parallel efficiency on distributed memory parallel architectures. Numerical studies are carried out using large-eddy simulation (LES). In LES, the scales larger than the grid are computed using a time- and space-accurate scheme, while the unresolved small scales are modeled using eddy viscosity based subgrid models. This is acceptable for the moment/energy closure since the small scales primarily provide a dissipative mechanism for the energy transferred from the large scales. However, for combustion to occur, the species must first undergo mixing at the small scales and then come into molecular contact. Therefore, global models cannot be used. Recently, a new model for turbulent combustion was developed, in which the combustion is modeled, within the subgrid (small-scales) using a methodology that simulates the mixing and the molecular transport and the chemical kinetics within each LES grid cell. Finite-rate kinetics can be included without any closure and this approach actually provides a means to predict the turbulent rates and the turbulent flame speed. The subgrid combustion model requires resolution of the local time scales associated with small-scale mixing, molecular diffusion and chemical kinetics and, therefore, within each grid cell, a significant amount of computations must be carried out before the large-scale (LES resolved) effects are incorporated. Therefore, this approach is uniquely suited for parallel processing and has been implemented on various systems such as: Intel Paragon, IBM SP-2, Cray T3D and SGI Power Challenge (PC) using the system independent Message Passing Interface (MPI) compiler. In this paper, timing data on these machines is reported along with some characteristic results.

Menon, Suresh↗
NASA NTRS1971

Solid propellant processing factor in rocket motor design

The ways are described by which propellant processing is affected by choices made in designing rocket engines. Tradeoff studies, design proof or scaleup studies, and special design features are presented that are required to obtain high product quality, and optimum processing costs. Processing is considered to include the operational steps involved with the lining and preparation of the motor case for the grain; the procurement of propellant raw materials; and propellant mixing, casting or extrusion, curing, machining, and finishing. The design criteria, recommended practices, and propellant formulations are included.

Source record↗
NASA NTRS1981

Strength distributions of SiC ceramics after oxidation and oxidation under load

The room-temperature strength distributions of a sintered and a hot-pressed SiC were examined as-machined, after oxidation at 1370 C, and after oxidation under load at 1370 C. The strengths were observed to be dependent on both the duration of oxidation and the magnitude of the applied load. Processes resulting in both strengthening and weakening behavior were observed to occur, at times simultaneously within the same strength distribution. This dynamic situation indicates that the strength-controlling flaw populations are highly transient in nature.

Easler, T. E.↗
NASA NTRS1983

Development of a systems theoretical procedure for evaluation of the work organization of the cockpit crew of a civil transport airplane

To achieve optimum design for the man machine interface with aircraft, a description of the interaction and work organization of the cockpit crew is needed. The development of system procedure to evaluate the work organization of pilots while structuring the work process is examined. Statistical data are needed to simulate sequences of pilot actions on the computer. Investigations of computer simulation and applicability for evaluation of crew concepts are discussed.

Fricke, M.↗
NASA NTRS1986

Detector Arrays With Image-Plane Processing

Image detection and edge processing merged on same VLSI chip. Concept combines relatively large detector with small processor elements as VLSI chip features and has many specific embodiments. New device concept retains relatively large detector element sizes (perhaps 10 to 100 micrometers or larger in diameter) with processing-electronics components of much smaller size (2 micrometers or less), filling in gaps left between detectorelement active areas. Detector-array device helps to perform difference-ofGaussian operation for use in machine-vision edge enhancement and edge detection.

Jobson, D. J.↗
NASA NTRS1985

Image gathering and processing - Information and fidelity

In this paper we formulate and use information and fidelity criteria to assess image gathering and processing, combining optical design with image-forming and edge-detection algorithms. The optical design of the image-gathering system revolves around the relationship among sampling passband, spatial response, and signal-to-noise ratio (SNR). Our formulations of information, fidelity, and optimal (Wiener) restoration account for the insufficient sampling (i.e., aliasing) common in image gathering as well as for the blurring and noise that conventional formulations account for. Performance analyses and simulations for ordinary optical-design constraints and random scences indicate that (1) different image-forming algorithms prefer different optical designs; (2) informationally optimized designs maximize the robustness of optimal image restorations and lead to the highest-spatial-frequency channel (relative to the sampling passband) for which edge detection is reliable (if the SNR is sufficiently high); and (3) combining the informationally optimized design with a 3 by 3 lateral-inhibitory image-plane-processing algorithm leads to a spatial-response shape that approximates the optimal edge-detection response of (Marr's model of) human vision and thus reduces the data preprocessing and transmission required for machine vision.

Huck, F. O.↗
NASA NTRS1985

Supercomputing on massively parallel bit-serial architectures

Research on the Goodyear Massively Parallel Processor (MPP) suggests that high-level parallel languages are practical and can be designed with powerful new semantics that allow algorithms to be efficiently mapped to the real machines. For the MPP these semantics include parallel/associative array selection for both dense and sparse matrices, variable precision arithmetic to trade accuracy for speed, micro-pipelined train broadcast, and conditional branching at the processing element (PE) control unit level. The preliminary design of a FORTRAN-like parallel language for the MPP has been completed and is being used to write programs to perform sparse matrix array selection, min/max search, matrix multiplication, Gaussian elimination on single bit arrays and other generic algorithms. A description is given of the MPP design. Features of the system and its operation are illustrated in the form of charts and diagrams.

Iobst, Ken↗
NASA NTRS1991

An unpredictable-dynamics approach to neural intelligence

The theoretical basis for a dynamic neural network architecture that takes advantage of the notion of terminal chaos to process information in a way that is phenomenologically similar to brain activity is presented. The architecture exploits the phenomenology of nonlinear dynamic systems as an alternative to the traditional paradigm of finite-state machines. It is based on some effects of non-Lipschitzian dynamics. The nonlinear phenomenon of terminal chaos and its relevance to brain activity are examined.

Zak, Michail↗
NASA NTRS2005

Generation and Performance of Automated Jarosite Mineral Detectors for Vis/NIR Spectrometers at Mars

Sulfate salt discoveries at the Eagle and Endurance craters in Meridiani Planum by the Mars Exploration Rover Opportunity have proven mineralogically the existence and involvement of water in Mars past. Visible and near infrared spectrometers like the Mars Express OMEGA, the Mars Reconnaissance Orbiter CRISM and the 2009 Mars Science Laboratory Rover cameras are powerful tools for the identification of water-bearing salts and other high priority minerals at Mars. The increasing spectral resolution and rover mission lifetimes represented by these missions currently necessitate data compression in order to ease downlink restrictions. On board data processing techniques can be used to guide the selection, measurement and return of scientifically important data from relevant targets, thus easing bandwidth stress and increasing scientific return. We have developed an automated support vector machine (SVM) detector operating in the visible/near-infrared (VisNIR, 300-2500 nm) spectral range trained to recognize the mineral jarosite (typically KFe3(SO4)2(OH)6), positively identified by the Mossbauer spectrometer at Meridiani Planum. Additional information is included in the original extended abstract.

Gilmore, M. S.↗
NASA NTRS2009

Design and Manufacturing of Tow-Steered Composite Shells Using Fiber Placement

Advanced composite shells that may offer the potential to improve the structural performance of future aircraft fuselage structures were developed under this joint NASA-industry collaborative effort. Two cylindrical shells with tailored, tow-steered layups and continuously varying fiber angle orientations were designed and built at the National Center for Advanced Manufacturing - Louisiana Partnership. The shells were fabricated from unidirectional IM7/8552 graphite-epoxy pre-preg slit tape material fiber-placed on a constant-diameter mandrel. Each shell had the same nominal 8-ply [plus or minus 45/plus or minus Theta]s layup, where the nominal fiber angle in the tow-steered plies varied continuously from 10 degrees along the crown to 45 degrees on each side, then back to 10 degrees on the keel. One shell was fabricated with all 24 tows placed during each pass of the fiber placement machine, resulting in many tow overlaps on the shell surface. The fiber placement machine's individual tow cut/restart capability was also used to manufacture a second shell with tow drops and a more uniform laminate thickness. This paper presents an overview of the detailed design and manufacturing processes for these shells, and discusses issues encountered during their fabrication and post-cure evaluation. Future plans for structural testing and analyses of the shells are also discussed.

Wu, K. Chauncey↗
NASA NTRS2005

Gears Based on Carbon Nanotubes

Gears based on carbon nanotubes (see figure) have been proposed as components of an emerging generation of molecular- scale machines and sensors. In comparison with previously proposed nanogears based on diamondoid and fullerene molecules, the nanotube-based gears would have simpler structures and are more likely to be realizable by practical fabrication processes. The impetus for the practical development of carbon-nanotube- based gears arises, in part, from rapid recent progress in the fabrication of carbon nanotubes with prescribed diameters, lengths, chiralities, and numbers of concentric shells. The shafts of the proposed gears would be made from multiwalled carbon nanotubes. The gear teeth would be rigid molecules (typically, benzyne molecules), bonded to the nanotube shafts at atomically precise positions. For fabrication, it may be possible to position the molecular teeth by use of scanning tunneling microscopy (STM) or other related techniques. The capability to position individual organic molecules at room temperature by use of an STM tip has already been demonstrated. Routes to the chemical synthesis of carbon-nanotube-based gears are also under investigation. Chemical and physical aspects of the synthesis of molecular scale gears based on carbon nanotubes and related molecules, and dynamical properties of nanotube- based gears, have been investigated by computational simulations using established methods of quantum chemistry and molecular dynamics. Several particularly interesting and useful conclusions have been drawn from the dynamical simulations performed thus far: The forces acting on the gears would be more sensitive to local molecular motions than to gross mechanical motions of the overall gears. Although no breakage of teeth or of chemical bonds is expected at temperatures up to at least 3,000 K, the gears would not work well at temperatures above a critical range from about 600 to about 1,000 K. Gear temperature could probably be controlled by use of coolant gases. For a given application, the gears would work well at temperatures below the critical range, provided that the rotational energy was less than the energy required to tilt the teeth through an angle of 20 . The predominant mechanism of gear failure would be slippage caused by tilting of teeth. Gears would resume functioning if the slipping gears were decelerated sufficiently.

Jaffe, Richard↗
NASA NTRS1993

CMOS Active Pixel Image Sensor

A new CMOS active pixel image sensor is reported. The sensor uses a 2.0mue double-poly, double-metal foundry CMOS process and is realized as a 28 x 28 array of 40 mue x 40 mue pixels. The Sensor features TTL compatible voltages, low noise and large dynamic range, and will be useful in machine vision and smart sensor applications.

CMOS↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS1999

Nondestructive Strain Measurement System Used to Determine Surface Strain on Fibers

Small-diameter structural fibers are being considered as reinforcements for high temperature ceramic matrix composite materials, and thus they require characterization. At the NASA Lewis Research Center, a nondestructive optical technique was used to determine surface strain on a structural fiber, in real time, as it was pulled in a tensile test machine. With this technique, interference or speckle patterns from the laser illuminated fiber test specimen are recorded. As the fiber is pulled, its speckle pattern shifts in proportion to the strain, translation, and rotation components of the sample deformation. Shifting speckle patterns are detected in real time by two linear charge-coupled discharge (CCD) camera arrays, and the images are processed by a hardware correlator. Surface strain is selectively detected on fibers with diameters on the order of 100 mm and can be resolved to 19 microstrain. This system was designed to be robust and compact and generally does not require surface preparation of the structural fibers. For strain detection, two laser beams are positioned incident on the structural fiber being tested, as shown in the photograph, where the test specimen is mounted in a tensile test machine via two coupons. As the fiber is pulled, the speckle pattern produced from each laser beam is detected by one of two CCD arrays located inside the tube on the right side of the photograph.

Tuma, Margaret L.↗
NASA NTRS1988

Semantic based man-machine interface for real-time communication

A flight expert system (FLES) was developed to assist pilots in monitoring, diagnosing and recovering from in-flight faults. To provide a communications interface between the flight crew and FLES, a natural language interface (NALI) was implemented. Input to NALI is processed by three processors: (1) the semantics parser; (2) the knowledge retriever; and (3) the response generator. First the semantic parser extracts meaningful words and phrases to generate an internal representation of the query. At this point, the semantic parser has the ability to map different input forms related to the same concept into the same internal representation. Then the knowledge retriever analyzes and stores the context of the query to aid in resolving ellipses and pronoun references. At the end of this process, a sequence of retrievel functions is created as a first step in generating the proper response. Finally, the response generator generates the natural language response to the query. The architecture of NALI was designed to process both temporal and nontemporal queries. The architecture and implementation of NALI are described.

Ali, M.↗
NASA NTRS1996

Investigation of ion-beam machining methods for replicated x-ray optics

The final figuring step in the fabrication of an optical component involves imparting a specified contour onto the surface. This can be expensive and time consuming step. The recent development of ion beam figuring provides a method for performing the figuring process with advantages over standard mechanical methods. Ion figuring has proven effective in figuring large optical components. The process of ion beam figuring removes material by transferring kinetic energy from impinging neutral particles. The process utilizes a Kaufman type ion source, where a plasma is generated in a discharge chamber by controlled electric potentials. Charged grids extract and accelerate ions from the chamber. The accelerated ions form a directional beam. A neutralizer outside the accelerator grids supplies electrons to the positive ion beam. It is necessary to neutralize the beam to prevent charging workpieces and to avoid bending the beam with extraneous electro-magnetic fields. When the directed beam strikes the workpiece, material sputters in a predicable manner. The amount and distribution of material sputtered is a function of the energy of the beam, material of the component, distance from the workpiece, and angle of incidence of the beam. The figuring method described here assumes a constant beam removal, so that the process can be represented by a convolution operation. A fixed beam energy maintains a constant sputtering rate. This temporally and spatially stable beam is held perpendicular to the workpiece at a fixed distance. For non-constant removal, corrections would be required to model the process as a convolution operation. Specific figures (contours) are achieved by rastering the beam over the workpiece at varying velocities. A unique deconvolution is performed, using series-derivative solution developed for the system, to determine these velocities.

Drueding, Thomas W.↗