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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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Analysis and performance prediction of digital computer facility servicing nonpredictable, randomly varying load in flight simulation
Algorithms for high-performance parallel computers are applied to perform static analyses of large-scale Space Station finite-element models (FEMs). Several parallel-vector algorithms under development at NASA Langley are assessed. Sparse matrix solvers were found to be more efficient than banded symmetric or iterative solvers for the static analysis of large-scale applications. In addition, new sparse and 'out-of-core' solvers were found superior to substructure (superelement) techniques which require significant additional cost and time to perform static condensation during global FEM matrix generation as well as the subsequent recovery and expansion. A method to extend the fast parallel static solution techniques to reduce the computation time for dynamic analysis is also described. The resulting static and dynamic algorithms offer design economy for preliminary multidisciplinary design optimization and FEM validation against test modes. The algorithms are being optimized for parallel computers to solve one-million degrees-of-freedom (DOF) FEMs. The high-performance computers at NASA afforded effective software development, testing, efficient and accurate solution with timely system response and graphical interpretation of results rarely found in industry. Based on the author's experience, similar cooperation between industry and government should be encouraged for similar large-scale projects in the future.
The dependence of the Bode response upon digital sample rate for first and second order linear continuous systems driven by a digital computer is derived. Open loop lead compensation introduced within the digital computer in order to increase the system bandwidth is also examined in terms of derived Bode responses. This introduction of lead terms within the digital computer is shown to be effective at operating frequencies below the Nyquist frequency. In most practical applications, empirical determination of these lead coefficients appears to be a sufficient compensation method.
A computational study was conducted to better understand experimental results obtained from wind tunnel tests of a Mach 4 waverider model and a comparative reference configuration. The experimental results showed that the performance of the reference configuration was slightly better than that of the waverider model. These results contradict waverider design theory, which suggests that a waverider optimized for maximum lift-to-drag should provide better performance than any other non-waverider configuration at a given design point, especially at hypersonic speeds. The computational results showed that the predicted surface pressure values and the integrated lift and drag coefficients from the pressure distributions were much lower for the reference model than for the flat-top model, due to the reference model bottom surface having a slight expansion. The lift-to-drag ratios for the flat-top model were higher due to a relatively low drag for the same amount of lift. These results indicate that the performance advantage of the reference model was due to the shape of the bottom surface and not due to the flat top surface. The results also showed that the reference model exhibited the same shock attachment characteristics as the waverider because the planform shapes were identical. CFD predictions show that the planform shape gives the waverider an advantage in performance over conventional hypersonic vehicles and that altering the bottom surface of a waverider does not cause significant performance degradation.
A computational study was conducted to better understand experimental results obtained from wind tunnel tests of a Mach 4 waverider model and a comparative reference configuration. The experimental results showed that the performance of the reference configuration was slightly better than that of the waverider model. These results contradict waverider design theory, which suggests that a waverider optimized for maximum lift-to-drag should provide better performance than any other non-waverider configuration at a given design point, especially at hypersonic speeds. The computational results showed that the predicted surface pressure values and the integrated lift and drag coefficients from the pressure distributions were much lower for the reference model than for the flat-top model, due to the reference model bottom surface having a slight expansion. The lift-to-drag ratios for the flat-top model were higher due to a relatively low drag for the same amount of lift. These results indicate that the performance advantage of the reference model was due to the shape of the bottom surface and not due to the flat top surface. The results also showed that the reference model exhibited the same shock attachment characteristics as the waverider because the planform shapes were identical. CFD predictions show that the planform shape gives the waverider an advantage in performance over conventional hypersonic vehicles and that altering the bottom surface of a waverider does not cause significant performance degradation.
Existing computer program improved capability for predicting performance of ducted fan in uniform axial flow
To ensure that robots are used effectively for exploration missions, it is important to assess their performance during operations. We are investigating the definition and computation of performance metrics for assessing remote robotic operations in real-time. Our approach is to monitor data streams from robots, compute performance metrics, and provide Web-based displays of these metrics for assessing robot performance during operations. We evaluated our approach for measuring robot performance with the K10 rovers from NASA Ames Research Center during a field test at Moses Lake Sand Dunes (WA) in June 2008. In this paper we present the results of evaluating our software for robot performance and discuss our conclusions from this evaluation for future robot operations.
We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOC's charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including, the software algorithms. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization.
Radioisotope Thermoelectric Generators (RTG) that will supply electrical power to the Galileo and International Solar Polar Mission (ISPM) spacecraft are exposed to several degradation mechanisms during the prolonged ground storage before launch. To assess the effect of storage on the RTG flight performance, a computer code has been developed which simulates all known degradation mechanisms that occur in an RTG during storage and flight. The modeling of these mechanisms and their impact on the RTG performance are discussed.
NASA missions push the limits of what is possible. The development of high-performance materials must keep pace with the agency's demanding, cutting-edge applications. Researchers at NASA's Ames Research Center are performing multiscale computational modeling to accelerate development times and further the design of next-generation aerospace materials. Multiscale modeling combines several computationally intensive techniques ranging from the atomic level to the macroscale, passing output from one level as input to the next level. These methods are applicable to a wide variety of materials systems. For example: (a) Ultra-high-temperature ceramics for hypersonic aircraft-we utilized the full range of multiscale modeling to characterize thermal protection materials for faster, safer air- and spacecraft, (b) Planetary entry heat shields for space vehicles-we computed thermal and mechanical properties of ablative composites by combining several methods, from atomistic simulations to macroscale computations, (c) Advanced batteries for electric aircraft-we performed large-scale molecular dynamics simulations of advanced electrolytes for ultra-high-energy capacity batteries to enable long-distance electric aircraft service; and (d) Shape-memory alloys for high-efficiency aircraft-we used high-fidelity electronic structure calculations to determine phase diagrams in shape-memory transformations. Advances in high-performance computing have been critical to the development of multiscale materials modeling. We used nearly one million processor hours on NASA's Pleiades supercomputer to characterize electrolytes with a fidelity that would be otherwise impossible. For this and other projects, Pleiades enables us to push the physics and accuracy of our calculations to new levels.
In spur gears both performance and failure predictions are known to be strongly dependent on the variation of load, lubricant film thickness, and total flash or contact temperature of the contacting point as it moves along the contact path. The need of an accurate tool for predicting these variables has prompted the development of a computer code based on recent findings in EHL and on finite element methods. The analyses and some typical results which to illustrate effects of gear geometry, velocity, load, lubricant viscosity, and surface convective heat transfer coefficient on the performance of spur gears are analyzed.
We give an overview of the operational concepts and architecture of the Kepler Science Data Pipeline. Designed, developed, operated, and maintained by the Science Operations Center (SOC) at NASA Ames Research Center, the Kepler Science Data Pipeline is central element of the Kepler Ground Data System. The SOC charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Data Pipeline, including the hardware infrastructure, scientific algorithms, and operational procedures. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center that hosts the computers required to perform data analysis. We discuss the high-performance, parallel computing software modules of the Kepler Science Data Pipeline that perform transit photometry, pixel-level calibration, systematic error-correction, attitude determination, stellar target management, and instrument characterization. We explain how data processing environments are divided to support operational processing and test needs. We explain the operational timelines for data processing and the data constructs that flow into the Kepler Science Data Pipeline.
Low power digital computer for spacecraft applications, discussing woven plated wire memory unit and English-like computer language
A computer-designed axisymmetric 2.4-cm-diameter four-stage depressed collectors was evaluated in conjunction with an octave bandwidth, dual-mode traveling wave tube (TWT). The TWT was operated over a wide range of conditions to simulate different applications. The collector performance was optimized (within the constraint of fixed collector geometry which was designed for operation of the TWT at saturation) over the range of TWT operating conditons covered. For operation of the dual-mode TWT at saturation, average collector efficiencies of 81 1/2 and 82 percent for the high and low modes, respectively, were obtained across an octave bandwidth, leading to a three-fold increase in the TWT overall efficiency. For operation of the TWT in the linear, low distortion range, collector efficiencies of 87 to 92 percent were obtained, leading to TWT overall efficiencies as high as 35 percent. For operation of the dual-mode TWT over a 10 to 1 range in output power, overall efficiencies of 14 to 41 percent were obtained.
Manual for computer program for predicting performance of ducted fans
Charged particle calibrations based on differenced range versus integrated Doppler (DRVID) are smoothed and fitted with a polynomial prior to its application in the orbit determination process. A description is given of the results of the tests performed on the computer program performing these calculations to determine its characteristics and to evaluate the acceptability of its computations. This program, called MEDIA, is described.
Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.
When porting sequential applications to parallel computer architectures, the program developer will typically go through several cycles of source code optimization and performance analysis. We have started a project to develop an environment where the user can jointly navigate through program structure and performance data information in order to make efficient optimization decisions. In a prototype implementation we have interfaced the CAPO computer aided parallelization tool with the Paraver performance analysis tool. We describe both tools and their interface and give an example for how the interface helps within the program development cycle of a benchmark code.