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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrated simulation of U-10Mo monolithic fuel swelling behavior

Here, a separate computational branch has been implemented within the DART (Dispersion Analysis Research Tool) computational code to simulate the swelling behavior of U-10Mo monolithic fuel under the operating conditions of high-power research and test reactors (RTRs). The monolithic branch of the DART code implements a mechanistic rate-theory-based fission-gas-behavior model for the calculation of fission gas swelling, as well as a suite of thermal, physical, and mechanical models to take into account various processes occurring in RTR fuels during irradiation. In order to accurately simulate and eventually predict U-10Mo monolithic fuel irradiation behavior, the code uses materials properties calculated with lower length-scale computational methods, such as gas atom diffusivity and U-Mo surface energy from atomic simulations and grain-morphology-specific recrystallization kinetics (recrystallized fuel volume fractions vs. fission density) predicted using the phase-field method. The remainder of fission gas behavior parameters used in the model were calibrated with measured intergranular bubble size distributions. With this integrated simulation approach, the swelling behavior of U-10Mo monolithic fuel was simulated for various initial grain sizes at different operating conditions and compared with measured data. Furthermore, because limited experimental data exist for parameter calibration detailed sensitivity studies for the important parameters used in the fission gas behavior model were performed in order to examine their impact on both intergranular gas bubble morphology at low fission density, and on total porosity at high fission density.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Asymptotic consistency of the WSINDy algorithm in the limit of continuum data

In this work we study the asymptotic consistency of the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy) in the identification of differential equations from noisy samples of solutions. We prove that the WSINDy estimator is unconditionally asymptotically consistent for a wide class of models that includes the Navier–Stokes, Kuramoto–Sivashinsky and Sine–Gordon equations. We thus provide a mathematically rigorous explanation for the observed robustness to noise of weak-form equation learning. Conversely, we also show that, in general, the WSINDy estimator is only conditionally asymptotically consistent, yielding discovery of spurious terms with probability one if the noise level exceeds a critical threshold σ c . We provide explicit bounds on σ c in the case of Gaussian white noise and we explicitly characterize the spurious terms that arise in the case of trigonometric and/or polynomial libraries. Furthermore, we show that, if the data is suitably denoised (a simple moving average filter is sufficient), then asymptotic consistency is recovered for models with locally-Lipschitz, polynomial-growth nonlinearities. Our results reveal important aspects of weak-form equation learning, which may be used to improve future algorithms. We demonstrate our findings numerically using the Lorenz system, the cubic oscillator, a viscous Burgers-growth model and a Kuramoto–Sivashinsky-type high-order PDE.

asymptotic consistency↗
NASA NTRS1981

Wing-alone aerodynamic characteristics at high angles of attack

An experimental investigation has been conducted to determine wing-alone supersonic aerodynamic characteristics at high angles of attack. The family of wings tested varied in aspect ratio from 0.5 to 4.0 and taper ratio from 0.0 to 1.0. The wings were tested at angles of attack ranging from 0 to 60 deg and Mach numbers from 1.6 to 4.6. The aerodynamic characteristics were obtained by integrating local pressures measured over the wing surface. Comparison of these data with the limited available data from the literature indicate the present data are free of sting interference effects through the test range of angle of attack. Presented and discussed are results showing the effects of model geometry, Mach number and angle of attack on aerodynamic characteristics consisting of normal force, pitching moment, bending moment, longitudinal center-of-pressure locations, and lateral center-of-pressure locations.

Stallings, R. L., Jr.↗
NASA NTRS1982

A comparison of computed Space Shuttle Orbiter surface pressures with flight measurements

Vehicles such as the Shuttle Orbiter operate over a wide range of angle of attack when entering the earth's atmosphere. This angle-of-attack range can be divided into two classes, a moderate regime, and a high regime for angles of attack greater than 30 deg. The HALIS (High Alpha Inviscid Solution) code was developed for computing flowfields in the high angle-of-attack regime. The present investigation is concerned with conducting a comparison of HALIS results with wind-tunnel data and the limited flight data from STS-1. The investigation shows that at high angle-of-attack, the axial flow over the entire windward surface can be subsonic, and, for those conditions, where the subsonic axial flow is confined to the nose of the vehicle, embedded regions of subsonic axial flow may be found along the vehicle strake tips.

Weilmuenster, K. J.↗
NASA NTRS1989

Blue compact dwarf galaxies in the Virgo Cluster - H I and IRAS data and upper limits on proto-dwarf galaxies

Correlations between various properties of blue compact dwarf (BCD) galaxies in the Virgo Cluster were studied. It was found that, at constant mass, BCD galaxies are comparable to non-BCD irregulars in both blue luminosity and H I flux. About one-third of the BCDs were detected by IRAS at 60 or 100 microns, mostly at intensities just above the threshold sensitivity.

Hoffman, G. Lyle↗
NASA NTRS2000

High Amplitude Acoustic Behavior of a Slit-Orifice Backed by a Cavity

The objective of this study was to acquire detailed acoustic data and limited flow visualization data for numerical validation of a new model of sound absorption by a very narrow rectangular slit backed by a cavity. The sound absorption model is that being developed by Dr. C. K. W. Tam of Florida State University. This report documents normal incidence impedance measurements of a singular rectangular slit orifice with no mean flow. All impedance measurements are made within a 1.12 inch (28.5 mm) diameter impedance tube using the two-microphone method for several frequencies in the range 1000 - 6000Hz and incident sound pressure levels in the range 130 - 150 dB. In the interest of leaving the analysis of the data to the developers of more advanced Analytical and computational models of sound absorption by narrow slits, we authors have refrained from giving our own explanations of the observed results, although many of the observed results can be explained using the classical understanding of sound absorption by orifices.

Ahuja, K. K.↗
NASA NTRS2005

Data-Acquisition Software for PSP/TSP Wind-Tunnel Cameras

Wing-Viewer is a computer program for acquisition and reduction of image data acquired by any of five different scientificgrade commercial electronic cameras used at Langley Research center to observe wind-tunnel models coated with pressure or temperature-sensitive paints (PSP/TSP). Wing-Viewer provides full automation of camera operation and acquisition of image data, and has limited data-preprocessing capability for quick viewing of the results of PSP/TSP test images. Wing- Viewer satisfies a requirement for a standard interface between all the cameras and a single personal computer: Written by use of Microsoft Visual C++ and the Microsoft Foundation Class Library as a framework, Wing-Viewer has the ability to communicate with the C/C++ software libraries that run on the controller circuit cards of all five cameras.

Amer, Tahani R.↗
NASA NTRS2021

HILIGT, Upper Limit Servers II - Implementing the Data Servers

The High-Energy Light curve Generator(HILIGT) is a new web-based tool which allows the user to generate long-term light curves of X-ray sources. It provides historical data and calculates upper limits from image data in real-time. HILIGT utilizes data from twelve satellites, both modern missions such as XMM-Newton and Swift, and earlier facilities such as ROSAT, EXOSAT, Einstein or Ariel V. Together, this enables the user to query 50 years of X-ray data and, for instance, study outburst behavior of transient sources. In this paper we focus on the individual back-end servers for each satellite, detailing the software layout, database design, catalog calls, and image footprints. We compile all relevant calibration information of these missions and provide an in-depth summary of the details of X-ray astronomical instrumentation and data.

Ole Konig↗
NASA NTRS1979

Aircraft motion analysis using limited flight and radar data

The development and application of methods for reconstructing, from a limited set of recorded data, a comprehensive scenario of aircraft motions before and during an accident are described. The accuracy of these analytical methods is investigated using data recorded onboard the Ames CV-990 research aircraft. In these experiments, the expanded set of data, derived from either foil or ATC records, is compared with corresponding values measured by the research instrumentation system onboard the aircraft. The results indicate that many of the derived quantities are in good agreement with the corresponding onboard measurements. A recent application of this procedure using actual accident records is presented and potential applications are briefly reviewed.

Wingrove, R. C.↗
NASA NTRS1979

Some aspects of a free jet phenomena to 105 L/D in a constant area duct

Under certain conditions, inlets with a Borda type geometry were shown to exhibit sufficiently strong separation effects to permit the working fluid to flow through the duct as if it were a free jet. Mass limiting flow data and associated pressure profiles for tubes of 14, 53, 64, 73, and 105 L/D with a Borda type inlet were taken to determine bounds of the free jet phenomena. For a given tube roughness, the limits appear to be one dimensional and dependent only on inlet stagnation conditions. For smooth tubes the upper L/D boundary is related by P sub R roughly equal to CT to the 17th power and sub R, C roughly equal to 00017 (L/D) to the 2.5 power where F sub R = P/P sub c is reduced pressure and T sub R = T/T sub c is reduced temperature. The lower bound appears to be saturation conditions at the inlet. Similar free jet effects were found for fluid hydrogen indicating that fluid jetting may be common to all fluids. While limited data on surface roughness show a decrease in the upper L/D limit, nevertheless fluid jetting still occurred.

Hendricks, R. C.↗
NASA NTRSDate not supplied

Summary of the NASA Semi-Span High-Lift Common Research Model Wind Tunnel Test at the QinetiQ 5-Metre Low-Speed Facility

A summary and selected results from experimental testing of the NASA 10% scale semi-span High-Lift Common Research Model (CRM-HL) at the QinetiQ 5m (5mWT) pressurized low-speed wind tunnel are presented. There were many objectives of this test, the primary being to define a set of reference configurations for dissemination and use within the CRM-HL ecosystem, one in the landing configuration and one in the takeoff configuration. A database of force, moment, pressure and surface flow visualization data was collected to support several objectives: aerodynamic sensitivity to high-lift device positioning, Reynolds number effects on configuration selection, isolation of the effects of various components of the reference configuration e.g. landing gear, nacelle, horizontal tail, etc., and limited repeatability data for uncertainty quantification assessment. Some results were compared to limited data collected at the NASA Langley Research Center 14x22 wind tunnel to assess half-model effects and facility differences. Half-model effects were further studied with use of floor blowing to change boundary layer profile and assess effects on model aerodynamics. Flow visualization data were gathered with fluorescent mini-tufts and UV oil. Finally, test time was spend working on the development of new Particle Image Velocimetry (PIV) and Model Deformation Measurement (MDM) capabilities at the 5mWT facility.

Ashley Evans↗
NASA NTRS2015

Common Cause Failure Modeling

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFS are a set of dependent type of failures that can be caused by: system environments; manufacturing; transportation; storage; maintenance; and assembly, as examples. Since there are many factors that contribute to CCFs, the effects can be reduced, but they are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and CCF (dependent) failure and data is limited, especially for launch vehicles. The Probabilistic Risk Assessment (PRA) of NASA's Safety and Mission Assurance Directorate at Marshall Space Flight Center (MFSC) is using generic data from the Nuclear Regulatory Commission's database of common cause failures at nuclear power plants to estimate CCF due to the lack of a more appropriate data source. There remains uncertainty in the actual magnitude of the common cause risk estimates for different systems at this stage of the design. Given the limited data about launch vehicle CCF and that launch vehicles are a highly redundant system by design, it is important to make design decisions to account for a range of values for independent and CCFs. When investigating the design of the one-out-of-two component redundant system for launch vehicles, a response surface was constructed to represent the impact of the independent failure rate versus a common cause beta factor effect on a system's failure probability. This presentation will define a CCF and review estimation calculations. It gives a summary of reduction methodologies and a review of examples of historical CCFs. Finally, it presents the response surface and discusses the results of the different CCFs on the reliability of a one-out-of-two system.

Hark, Frank↗
NASA NTRS2016

Common Cause Failure Modeling

Common Cause Failures (CCFs) are a known and documented phenomenon that defeats system redundancy. CCFS are a set of dependent type of failures that can be caused by: system environments; manufacturing; transportation; storage; maintenance; and assembly, as examples. Since there are many factors that contribute to CCFs, the effects can be reduced, but they are difficult to eliminate entirely. Furthermore, failure databases sometimes fail to differentiate between independent and CCF (dependent) failure and data is limited, especially for launch vehicles. The Probabilistic Risk Assessment (PRA) of NASA's Safety and Mission Assurance Directorate at Marshal Space Flight Center (MFSC) is using generic data from the Nuclear Regulatory Commission's database of common cause failures at nuclear power plants to estimate CCF due to the lack of a more appropriate data source. There remains uncertainty in the actual magnitude of the common cause risk estimates for different systems at this stage of the design. Given the limited data about launch vehicle CCF and that launch vehicles are a highly redundant system by design, it is important to make design decisions to account for a range of values for independent and CCFs. When investigating the design of the one-out-of-two component redundant system for launch vehicles, a response surface was constructed to represent the impact of the independent failure rate versus a common cause beta factor effect on a system's failure probability. This presentation will define a CCF and review estimation calculations. It gives a summary of reduction methodologies and a review of examples of historical CCFs. Finally, it presents the response surface and discusses the results of the different CCFs on the reliability of a one-out-of-two system.

Hark, Frank↗
NASA NTRS2021

Spatio-Temporal Patterns of Optimal Landsat Data for Burn Severity Index Calculations: Implications for High Northern Latitudes Wildfire Research

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differ-enced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of sub-optimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as “optimal”. In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made.

Dong Chen↗
DOE OSTI2022

Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.

08 HYDROGEN↗
DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗