Surface Meteorological Station - PNNL 10m Sonic, Physics site-4 - Reviewed Data
This dataset provides fast response wind and virtual sonic temperature data.
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This dataset provides fast response wind and virtual sonic temperature data.
This dataset provides fast response wind and virtual sonic temperature data.
In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.
Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, $γ$ -rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt. 61, RDS1 (2022). Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4D + ). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.
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A ‘Phase Space’ is a mathematical representation of all possible physical states of matter of a physical volume or system. Particle beams at Fermilab exist in a six-dimensional phase space: three position dimensions, and three momentum dimensions. To ‘reconstruct’ that space means to take measurement data from virtual detectors along those beams and map out where particles are and how they’re moving, using computational methods. In this experiment, we simulated a particle beam (Fig 1) and used the position distribution of each particle at each detector to map out the magnitude of the beam’s initial momentum.
Ionospheric electron density profiles from procedures permitting accurate extraction of virtual height data
Analysis of the radio-tracking data from Mariner 10 yields 6,023,600 plus or minus 600 for the ratio of the mass of the sun to that of Mercury, in very good agreement with values determined earlier from radar data alone. Occultation measurements yielded values for the radius of Mercury of 2440 plus or minus 2 and 2438 plus or minus 2 kilometers at latitudes of 2 N and 68 N, respectively, again in close agreement with the average equatorial radius of 2439 plus or minus 1 kilometers determined from radar data. The mean density of 5.44 grams per cubic centimeter deduced for Mercury from Mariner 10 data thus virtually coincides with the prior determination. No evidence of either an ionosphere or an atmosphere was found.
A simple, yet effective procedure for the geometric correction of partial Landsat scenes is described. The procedure is based on the acquisition of actual and virtual control points from the line printer output of enhanced curvilinear features. The accuracy of this method compares favorably with that of the conventional approach in which an interactive image display system is employed.
The development of a vectorized computer code for the solution of the three-dimensional viscous-compressible Navier-Stokes equations is described. The code is applied on the CDC STAR-100 vector computer which is capable of achieving high result rates when a high degree of parallelism is present in the computations. The computational technique is an explicit time-split MacCormack predictor-corrector algorithm. Since a large volume of data is processed and virtual memory utilized, a data management scheme based on interleaving is used. The program has been applied to obtain the solution of the laminar supersonic flow about a family of three-dimensional corners. The equations of motion are expressed in a generalized form relative to a uniform rectangular computational domain. The metric coefficient and boundary conditions must be supplied for the corresponding physical domain. For calculations with 30,000 grid points, a computational rate of 0.00015 seconds per grid point per time step is observed.
Fast computer-controlled sampling of optical knife-edge response (KER) signal increases accuracy of optical system aberration measurement. Submicrosecond-response detectors in optical focal plane convert optical signals to electrical signals converted to digital data, sampled and feed into computer for storage and subsequent analysis. Optical data are virtually free of effects of index-of-refraction gradients.
Economical archival and retrieval of image data is becoming increasingly important considering the unprecedented data volumes expected from the Earth Observing System (EOS) instruments. For cost effective browsing the image data (possibly from remote site), and retrieving the original image data from the data archive, we suggest an integrated image browse and data archive system employing incremental transmission. We produce our browse image data with the JPEG/DCT lossy compression approach. Image residual data is then obtained by taking the pixel by pixel differences between the original data and the browse image data. We then code the residual data with a form of variable length coding called diagonal coding. In our experiments, the JPEG/DCT is used at different quality factors (Q) to generate the browse and residual data. The algorithm has been tested on band 4 of two Thematic mapper (TM) data sets. The best overall compression ratios (of about 1.7) were obtained when a quality factor of Q=50 was used to produce browse data at a compression ratio of 10 to 11. At this quality factor the browse image data has virtually no visible distortions for the images tested.
The objectives of this study were to: (1) understand the phenomenon of continuous free oscillations of the Earth and (2) examine the idea of using this phenomenon for planetary seismology. We first describe the results on (1) and present our evaluations of the idea (2) in the final section. In 1997, after almost forty years since the initial attempt by Benioff et al, continuous free oscillations of the Earth were discovered. Spheroidal fundamental modes between 2 and 7 millihertz are excited continuously with acceleration amplitudes of about 0.3-0.5 nanogals. The signal is now commonly found in virtually all data recorded by STS-1 type broadband seismometers at quiet sites. Seasonal variation in amplitude and the existence of two coupled modes between the atmosphere and the solid Earth support that these oscillations are excited by the atmosphere. Stochastic excitation due to atmospheric turbulence is a favored mechanism, providing a good match between theory and data. The atmosphere has ample energy to support this theory because excitation of these modes require only 500-10000 W whereas the atmosphere contains about 117 W of kinetic energy. An application of this phenomenon includes planetary seismology, because other planets may be oscillating due to atmospheric excitation. The interior structure of planets could be learned by determining the eigenfrequencies in the continuous free oscillations. It is especially attractive to pursue this idea for tectonically quiet planets, since quakes may be too infrequent to be recorded by seismic instruments.
The University of Denver Focused Cavity Aerosol Spectrometer (FCAS 11) and Nucleation-Mode Aerosol Size Spectrometer (N-MASS) were successfully integrated and flown aboard NASA s DC-8 for the second SAGE I11 Ozone Loss and Validation Experiment (SOLVE 11). Both instruments performed well during SOLVE, with virtually complete data coverage for all mission and test flights. The few exceptions to this were the occasional simultaneous zero-check for the instruments, and some data loss for channel 4 of the N-MASS. The only consequence of the latter is reduced resolution in the 15 to 60 nm range for the affected size distributions.
Contents include the folowing: Virtual NASA Data Center (NDC) overview. NDC challenges. NDC present and future. NDC environment.NDC decomposition NDC opportunities. NDC solution: Horizontally and vertically scalable. Adaptable to new business/mission areas. Competitive-cost and quality. Superior service delivery.
Wave phenomena, ranging from freely propagating electromagnetic radiation (e.g., solar radio bursts, AKR) to plasma wave modes trapped in various plasma regimes (e.g., whistlers, Langmuir and ULF waves) and atmospheric gravity waves, are ubiquitous in the heliosphere. Because waves can propagate, wave data obtained at a given observing location may pertain to wave oscillations generated locally or from afar. While wave data analysis requires knowledge of wave characteristics specific to different wave modes, the search for appropriate data for heliophysics wave studies also requires knowledge of wave phenomena. In addition to deciding whether the interested wave activity is electrostatic (i.e., locally trapped) or electromagnetic (with propagation over distances), considerations must be given to the dependence of the wave activity on observer's location or viewing geometry, propagating frequency range and whether the wave data were acquired by passive or active observations. Occurances of natural wave emissions i the magnetosphere (e.g, auroral kilometric radiation) are often dependent also on the state (e.e., context) of the magnetosphere that varies with the changing solar wind, IMF and geomagnetic conditions. Fung and Shao [2008] showed recently that magnetospheric state can be specified by a set of suitably time-shifted solar wind, IMF and the multi-scale geomagnetic response parameters. These parameters form a magnetospheric state vector that provides the basis for searching magnetospheric wave data by their context conditions. Using the IMAGE Radio Plasma Imager (RPI) data and the NASA Magnetospheric State Query System (MSOS) [Fung, 2004], this presentation demonstrates the VWO context data search capability under development and solicits feedback from the Heliophysics research community for improvements.
The TES-8 was jettisoned from the International Space Station on January 31, 2019. As an orbital laboratory and 8th in on-going series, the design makes use of a standard set of interfaces and safety features that permit rapid re-flight. On this flight, an advanced Exo-Brake is flown with de-orbit targeting capability that will engender sample return capability from LEO platforms. A Virtual Reality data recording system uses stereo imaging and efficient data-compression with an NVIDIA GPU (Graphics Processing Unit) to permit compression and transmission of very large data files. An SDR (Software Defined Radio) will download data to the NEN (Near Earth Network) for the first time - demonstrating potential use in cis-lunar space using S-band. For the first time, a comparison will be made regarding the functionality of the Iridium and Globalstar short burst data modems - as essential communication tools for future nano-sat projects. Lastly, the 7 micro-processors and 4 cameras provide an excellent learning platform for university students and NASA young professionals.
Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.