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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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Please check the README.txt file from the supplementary files for more information about the dataset.
Please check the README.txt file from the supplementary files for more information about the dataset.
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The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, must maintain time and frequency within specified limits in order to accurately track the spacecraft engaged in deep space exploration. Various methods are used to coordinate the clocks among the three tracking complexes. These methods include Loran-C, TV Line 10, Very Long Baseline Interferometry (VLBI), and the Global Positioning System (GPS). Calculations are made to obtain frequency offsets and Allan variances. These data are analyzed and used to monitor the performance of the hydrogen masers that provide the reference frequencies for the DSN Frequency and Timing System (DFT). Areas of discussion are: (1) a brief history of the GPS timing receivers in the DSN, (2) a description of the data and information flow, (3) data on the performance of the DSN master clocks and GPS measurement system, and (4) a description of hydrogen maser frequency steering using these data.
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The purpose of the present study was to identify the specific cause of the turbulence that affected flights JAL 042 and JAL 046. This has been accomplished by expanding on the NTSB analysis to include a detailed examination of digital Flight Data Recorder (DFDR) information available from both JAL 042 and JAL 046 and the ANC rawinsonde.
A heat-transfer investigation was conducted with air flowing through an electrically heated silicon carbide tube with a rounded entrance, an inside diameter of 3/4 inch, and effective heat-transfer length of 12 inches over a range of Reynolds numbers up to 300,000 and a range of average inside-tube-wall temperatures up to 2500 R. The highest corresponding local outside-tube-wall temperature was 3010 R.
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Data from the "Turbulence Modeling Resource" website for turbulent flow over an NACA-0012 airfoil is analyzed to determine the convergence behavior of three second-order CFD (Computational Fluid Dynamics) codes: CFL3D (Computational Fluids Lab 3 Dimensional flow solver), FUN3D (Fully Unstructured Navier-stokes flow solver), and TAU (German Aerospace Center (DLR) 2 dimensional code for unstructured hybrid grids solving the Reynolds-Averaged Navier-Stokes equations or the Euler equations). The convergence of both integrated properties and pointwise data are examined. Several different methods for estimating errors and computing convergence rates are compared. A high-order extension to the Richardson extrapolation is developed that improves the accuracy of the mesh limit values and provides a quantitative estimate of the threshold of the asymptotic regime. The coefficient of total drag exhibits second-order convergence for all three codes, and convergence is monotone over a sequence of 7 grids. Other force coefficients are not so well behaved. The convergence rates of the viscous component of drag on the three nest grids ranges from 3:0 for CFL3D to 1:0 for FUN3D. The three codes are converging to similar but not identical solutions. The largest differences between the codes are in the coefficient of lift for which the difference between CFL3D and FUN3D is greater than 10 (sup minus 4). The best agreement occurs in the viscous component of drag, which is the only force component for which all three codes are converging towards each other at a rate of second-order. The agreement between the two unstructured grid codes is good with all properties except lift converging towards common values at a rate of second-order. No one code was universally better than the other. The TAU code has the lowest error in total drag, FUN3D has the lowest error in lift, and CFL3D has the lowest error in the viscous component of drag.
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the inputs. Consequently, the overall formulation sets up a procedure to predict the hyperparameters of this distribution which are then used to compute an objective function given training data. We demonstrate that this framework has the ability to provide for prediction confidence intervals based on the assumption of a probabilistic posterior, given an appropriate model architecture and adequate training data. The applicability of the present framework to cases with noisy measurements and limited observations is also assessed. To demonstrate the capabilities of this framework, we consider canonical regression problems of fluid dynamics from the viewpoint of reduced-order modeling and spatial data recovery for four canonical data sets. The examples considered in this study arise from (i) the shallow-water equations, (ii) a two-dimensional cylinder flow, (iii) the wake of a NACA0012 airfoil with a Gurney flap, and (iv) the NOAA sea surface temperature data set. Furthermore, the present results indicate that the probabilistic neural network not only produces a machine-learning-based fluid flow surrogate model but also systematically quantifies the uncertainty therein to assist with model interpretability.
Experimental data for a series of two- and three-dimensional shock wave/turbulent boundary layer interaction flows at Mach 7 are presented. Test bodies, composed of simple geometric shapes, were designed to generate flows with varying degrees of pressure gradient, boundary-layer separation, and turning angle. The data include surface-pressure and heat-transfer distributions as well as limited mean-flow-field surveys in both the undisturbed and the interaction regimes. The data are presented in a convenient form for use in validating existing or future computational models of these generic hypersonic flows.
AZ Technology has been working with NASA MSFC (Marshall Space Flight Center) to find ways to make it easier for remote experimenters (RPI's) to monitor their International Space Station (ISS) payloads in real-time from anywhere using standard/familiar devices. That effort resulted in a product called 'EZStream' which is in use on several ISS-related projects. Although the initial implementation is geared toward ISS, the architecture and lessons learned are applicable to other space-related programs. This paper begins with a brief history on why Internet-based real-time data is important and where EZStream or products like it fit in the flow of data from orbit to experimenter/researcher. A high-level architecture is then presented along with explanations of the components used. A combination of commercial-off-the-shelf (COTS), Open Source, and custom components are discussed. The use of standard protocols is shown along with some details on how data flows between server and client. Some examples are presented to illustrate how a system like EZStream can be used in real world applications and how care was taken to make the end-user experience as painless as possible. A system such as EZStream has potential in the commercial (non-ISS) arena and some possibilities are presented. During the development and fielding of EZStream, a lot was learned. Good and not so good decisions were made. Some of the major lessons learned will be shared. The development of EZStream is continuing and the future of EZStream will be discussed to shed some light over the technological horizon.
Experimental data are analyzed to support theoretical predictions for discharge coefficients in circular-arc venturi flow meters operating in the critical sonic flow regime at throat Reynolds numbers above 150 thousand. The data tend to verify the predicted 0.25% decrease in the discharge coefficient during transition from a laminar to turbulent boundary layer. Four different test gases and three flow measurement facilities were used in the experiments with 17 venturis with throat sizes from 0.15 to 1.37 in. and Beta ratios ranging from 0.014 to 0.25. Recommendations are given as to how the effectiveness of future studies in the field could be improved.