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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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Data and performance of flow generation rotor and single-slotted 0.75 hub diffusion factor stator
Test on overall and blade element performance of single slotted 0.65 hub diffusion factor stator with self energized blowing boundary layer control
Boundary layer hub diffusion-factor stator blowing technique
Satellite gravity gradiometers, particularly the two stage drag free carrier vehicle are discussed. An inner stage, carrying the tracking antenna(s), measures the relative position of the internal free proof mass, and feeds this to a set of magnetic forcers, acting against the outer or main vehicle. As the external forces on the inner stage are low, and as the position relative to the proof mass is tightly controlled, carrier phase disturbances are greatly reduced. The arrangement lowers instantaneous accelerations. It is stressed that gravity gradiometers do not measure gradients, they measure components of an intrinsic tensor.
An assessment of APNASA was conducted at NASA Glenn Research Center under the Fundamental Aeronautics Program to determine their predictive capabilities. The geometry selected for this study was Stage 35 which is a single stage transonic compressor. A speedline at 100% speed was generated and compared to experimental data at 100% speed for two turbulence models. Performance of the stage at 100% speed and profiles of several key aerodynamic parameters are compared to the survey data downstream of the stator in this report. In addition, hub leakage was modeled and compared to solutions without leakage and the available experimental data.
Using new software, nighttime thermal maps of the lunar surface have been generated from data obtained by the Apollo 17 Infrared Scanning Radiometer (ISR) in lunar orbit. Most of the thermal anomalies observed in the maps correspond to fresh lunar craters because blocks on the lunar surface maintain a thermal contrast relative to surrounding soil during the lunar night. Craters of Erastosthenian age and older - relatively young by lunar standards - have developed soil covers that make them almost indistinguishable from their surroundings in the thermal data. Thermal images of Copernican age craters show various stages of a degradation process, allowing the craters to be ranked by age. The ISR data should yield insights into lunar surface evolution as well as a more detailed understanding of the bombardment history after formation of the great mare basins.
This paper reports on the integration of a lossless data compression/decompression chipset into a space data system architecture. For its compression engine, the data system incorporates the Universal Source Encoder (USE) designed for the NASA/Goddard Space Flight Center. Currently, the data compression testbed generates video frames consisting of 512 lines of 512 pixels having 8-bit resolution. Each image is passed through the USE where the lines are internally partitioned into 16-word blocks. These blocks are adaptively encoded across widely varying entropy levels using a Rice 12-option set coding algorithm. The current system operates at an Input/Output rate of 10 Msamples/s or 80 Mbits/s for each buffered input line. Frame and line synchronization for each image are maintained through the use of uniquely decodable command words. Length information of each variable length compressed image line is also included in the output stream. The data and command information are passed to the next stage of the system architecture through a serial fiber-optic transmitter. The initial segment of this stage consists of packetizer hardware which adds an appropriate CCSDS header to the received source data. An uncompressed mode is optionally available to pass image lines directly to the packetizer hardware. A data decompression testbed has also been developed to confirm the data compression operation.
On five occasions spanning the ACE-Asia field experiment in spring 2001, the multi-angle imaging MISR instrument, flying aboard the NASA Earth Observing System s Terra satellite, took quarter-kilometer data over a 400-km-wide swath, coincident with high-quality observations by multiple instruments on two or more participating surface and airborne platforms. The cases capture a range of clean, polluted, and dusty aerosol conditions. They represent some of the best opportunities during ACE- Asia for comparative studies among intensive and extensive aerosol observations in their environmental context. We inter-compare related measurements and discuss the implications of apparent discrepancies for each case, at a level of detail appropriate to the analysis of satellite observations. With a three-stage optical modeling process, we synthesize data from multiple sources into layer-by-layer snapshots that summarize what we know about the state of the atmosphere and surface at key locations during each event, to be used for satellite vicarious calibration and aerosol retrieval validation. Aerosols within a few kilometers of the surface were composed primarily of pollution and Asian dust mixtures, as expected. Accumulation and coarse-mode particle size distributions varied little among the events studied, but column aerosol optical depth changed by more than a factor of four, and the near-surface proportion of dust ranged from about 25% to 50%. The amount of absorbing material in the sub-micron fraction was highest when near-surface winds crossed Beijing and the Korean Peninsula, and was considerably lower for all other cases. Ambiguities remain in segregating size distributions by composition; having simultaneous single scattering albedo measurements at more than a single wavelength would significantly reduce the resulting optical model uncertainties, as would integral constraints from surface and atmospheric radiative flux observations. The consistency of component particle micro-physical properties among the five events, even in this relatively complex aerosol environment, suggests that global, satellite-derived maps of aerosol-air-mass-type extent, combined with targeted in situ measurements, can provide a detailed global picture of aerosol behavior. Further joint satellite and in situ analysis is needed to assess the spatial variability of both intensive and extensive aerosol properties within aerosol air masses in two spatial dimensions.
In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.
Microgrids can experience frequency instability while operating in the islanded mode due to a significant demand generation imbalance. Presence of net metered (NM) loads can further complicate accurate demand forecasting. For these loads only the net demand, difference of load demand and generation, is available. This paper presents the various stages observed during microgrid restoration with high penetration of NM loads. Disaggregation of demand and generation from the net metered time series is shown to be essential for accurate demand forecasting in each of these stages. A disaggregation approach is proposed using correlation in the net metered data. Time series analysis is then used to forecast the demand in each stage of the microgrid restoration process. A case study using demand and generation time series data from residential loads, shows that the proposed approach is significantly more accurate for demand forecasting than using only the net-metered time series.
This paper presents detailed phase-resolved heat-flux data obtained on rotor blades and a comparison of simultaneously obtained time-resolved heat-flux and static pressure data obtained on the stationary shroud of a Garrett TFE 731-2 HP full-stage rotating turbine. A shock tube is used to generate a short-duration source of heated and pressurized air and platinum thin-film gages are used to obtain heat-flux measurements. Blade results are presented at several selected blade locations. Shroud surface pressure and heat-flux time histories are presented for comparable locations relative to the blade position.
Data centers have grown in physical size and their electrical power consumption has grown to levels of several 100kW and approaching 1 GW in large installations. The low voltage electrical distribution inside of Data Centers consists of several conversion stages and a lot of wiring to bring the power from medium voltage (MV) levels outside of the building to the low voltage levels that the servers and racks require. Energy losses during distribution and conversion and high incident arc flash energy levels at the point of use are significant. The electrical distribution system is complex and costly.
Accurate prediction of hardware and flow characteristics within the Space Shuttle Main Engine (SSME) during transient and main-stage operation requires a significant integration of ground test data, flight experience, and computational models. The process of integrating SSME test measurements with physical model predictions is commonly referred to as data reduction. Uncertainties within both test measurements and simplified models of the SSME flow environment compound the data integration problem. The first objective of this effort was to establish an acceptability criterion for data reduction solutions. The second objective of this effort was to investigate the data reduction potential of the ROCETS (Rocket Engine Transient Simulation) simulation platform. A simplified ROCETS model of the SSME was obtained from the MSFC Performance Analysis Branch . This model was examined and tested for physical consistency. Two modules were constructed and added to the ROCETS library to independently check the mass and energy balances of selected engine subsystems including the low pressure fuel turbopump, the high pressure fuel turbopump, the low pressure oxidizer turbopump, the high pressure oxidizer turbopump, the fuel preburner, the oxidizer preburner, the main combustion chamber coolant circuit, and the nozzle coolant circuit. A sensitivity study was then conducted to determine the individual influences of forty-two hardware characteristics on fourteen high pressure region prediction variables as returned by the SSME ROCETS model.
To support development of a zero-gravity pressure control capability for liquid hydrogen (LH2), a series of thermodynamic venting system (TVS) tests was conducted in 1996 and 1998 using the Marshall Space Flight Center (MSFC) multipurpose hydrogen test bed (MHTB). These tests were performed with ambient heat leaks =20 and 50 W for tank fill levels of 90%, 50%, and 25%. TVS performance testing revealed that the spray bar was highly effective in providing tank pressure control within a 7-kPa band (131-138 Wa), and complete destratification of the liquid and the ullage was achieved with all test conditions. Seven of the MHTB tests were correlated with the TVS performance analytical model. The tests were selected to encompass the range of tank fill levels, ambient heat leaks, operational modes, and ullage pressurants. The TVS model predicted ullage pressure and temperature and bulk liquid saturation pressure and temperature obtained from the TVS model were compared with the test data. During extended self-pressurization periods, following tank lockup, the model predicted faster pressure rise rates than were measured. However, once the system entered the cyclic mixing/venting operational mode, the modeled and measured data were quite similar.
The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.
Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40-60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.
The Project 8 collaboration seeks to measure, or more tightly bound, the mass of the electron antineutrino by applying a novel spectroscopy technique to precisely measure the tritium beta-decay spectrum. The current system produces a single analog signal, which is digitized and processed in several stages before being saved to local disk storage. Online processing includes two stages, an FPGA connected to the analog to digital converter reduces the data down to the region of interest before shipping over the local network for further processing and storage. A normal CPU-based processing stage applies triggering logic to only save data at times when a signal is present, further reducing the total volume of data which needs to be written to disk or transferred for long-term storage. The next stage of the project will need to process many input channels and will integrate a necessary aggregation and combination step prior to applying the event search and triggering logic. We present the online processing system which has successfully been deployed for the current, singlechannel, phase. We also present the status and design for a many-channel platform.
This paper details orbit determination techniques and strategies employed within each stage of the larger iterative process of preprocessing raw GRAIL data into the gravity science measurements used within gravity field solutions. Each orbit determination pass used different data, corrections to them, and/or estimation parameters. We compare performance metrics among these passes. For example, for the primary mission, the magnitude of residuals using our orbits progressed from approximately or equal to19.4 to 0.077 approximately or equal to m/s for inter-satellite range rate data and from approximately or equal to 0.4 to approximately or equal to 0.1 mm/s for Doppler data.