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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 469 records · Page 26

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NASA NTRS1994

A dynamic method for magnetic torque measurement

In a magnetic suspension system, accurate force measurement will result in better control performance in the test section, especially when a wider range of operation is required. Although many useful methods were developed to obtain the desired model, however, significant error is inevitable since the magnetic field distribution of the large-gap magnetic suspension system is extremely nonlinear. This paper proposed an easy approach to measure the magnetic torque of a magnetic suspension system using an angular photo encoder. Through the measurement of the velocity change data, the magnetic torque is converted. The proposed idea is described and implemented to obtain the desired data. It is useful to the calculation of a magnetic force in the magnetic suspension system.

Lin, C. E.↗

NASA NTRS1994

Analyses and forecasts with LAWS winds

Horizontal fluxes of atmospheric water vapor are studied for summer months during 1989 and 1992 over North and South America based on analyses from European Center for Medium Range Weather Forecasts, US National Meteorological Center, and United Kingdom Meteorological Office. The calculations are performed over 20 deg by 20 deg box-shaped midlatitude domains located to the east of the Rocky Mountains in North America, and to the east of the Andes Mountains in South America. The fluxes are determined from operational center gridded analyses of wind and moisture. Differences in the monthly mean moisture flux divergence determined from these analyses are as large as 7 cm/month precipitable water equivalent over South America, and 3 cm/month over North America. Gridded analyses at higher spatial and temporal resolution exhibit better agreement in the moisture budget study. However, significant discrepancies of the moisture flux divergence computed from different gridded analyses still exist. The conclusion is more pessimistic than Rasmusson's estimate based on station data. Further analysis reveals that the most significant sources of error result from model surface elevation fields, gaps in the data archive, and uncertainties in the wind and specific humidity analyses. Uncertainties in the wind analyses are the most important problem. The low-level jets, in particular, are substantially different in the different data archives. Part of the reason for this may be due to the way the different analysis models parameterized physical processes affecting low-level jets. The results support the inference that the noise/signal ratio of the moisture budget may be improved more rapidly by providing better wind observations and analyses than by providing better moisture data.

Wang, Muyin↗

NASA NTRS1997

Assimilation of Satellite Data in Regional Air Quality Models

In terms of important uncertainty in regional-scale air-pollution models, probably no other aspect ranks any higher than the current ability to specify clouds and soil moisture on the regional scale. Because clouds in models are highly parameterized, the ability of models to predict the correct spatial and radiative characteristics is highly suspect and subject to large error. The poor representation of cloud fields from point measurements at National Weather Services stations and the almost total absence of surface moisture availability observations has made assimilation of these variables difficult to impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry.

SPATIAL DISTRIBUTION↗

NASA NTRS1994

On the Validation of Satellite-Derived Sea Ice Surface Temperature

The surface temperature of sea ice controls the rate of ice growth and heat exchange between the ocean and the atmosphere. An algorithm for the satellite retrieval of ice surface temperature has recently been published, but due to the lack of validation data has not been extensively tested. In this paper, data from a recent Arctic field experiment is used in an attempt to validate that algorithm. While the procedure is, in principle, straightforward, we demonstrate that validation is complicated by a variety of factors, including incorrectly assumed atmospheric conditions, undetected clouds in the satellite data, spatial and temporal variability in the surface temperature field, and surface and satellite measurement errors. Comparisons between surface temperatures determined from upwelling broadband longwave radiation, spatial measurements of narrow-band radiation, thermocouples buried just below the snow surface, and narrow-band satellite data show differences of 1 to 3 deg. C. The range in these independent measurements indicates the need for specially designed validation experiments utilizing narrow-band radiometers on aircraft to obtain broad spatial coverage.

Key, J.↗

NASA NTRS2000

[Measurement of Speed and Direction of Ocean Surface Winds Using Quik Scat Scatterometer]

The SeaWinds on QuikSCAT scatterometer was developed by NASA JPL to measure the speed and direction of ocean surface winds. Simulations performed to estimate the performance of the instrument prior to its launch have indicated that the mid-swath accuracy is worse than that of the rest of the swath. This behavior is a general characteristic of scanning pencil beam scatterometers. For SeaWinds, the accuracy of the rest of the swath, and the size of the swath are such that the instrument meets its science requirements despite mid-swath shortcomings. However, by understanding the problem at mid-swath, we can improve the performance there as well. We discuss the underlying causes of the problem in detail and propose a new wind retrieval algorithm which improves mid-swath performance. The directional discrimination ability of the instrument varies with cross track distance wind speed, and direction. By estimating the range of likely wind directions for each measurement cell, one can optimally apply information from neighboring cells where necessary in order to reduce random wind direction errors without significantly degrading the resolution of the resultant wind field. In this manner we are able to achieve mid-swath RMS wind direction errors as low as 15 degrees for low winds and 10 degrees for moderate to high winds, while at the same time preserving high resolution structures such as cyclones and fronts.

Stiles, Bryan↗

NASA NTRS1998

Mars Rover Navigation Results Using Sun Sensor Heading Determination

Upcoming missions to the surface of Mars will use mobile robots to traverse long distances from the landing site. To prepare for these missions, the prototype rover, Rocky 7, has been tested in desert field trials conducted with a team of planetary scientists. While several new capabilities have been demonstrated, foremost among these was sun-sensor based traversal of natural terrain totaling a distance of one kilometer. This paper describes navigation results obtained in the field tests, where cross-track error was only 6% of distance traveled. Comparison with previous results of other planetary rover systems shows this to be a significant improvement.

Volpe, Richard↗

NASA NTRS2000

A Probabilistic Technique for Improving the Mid-Swath Wind Direction Accuracy of Scanning Pencil Beam Scatterometers

The SeaWinds on QuikSCAT scatterometer is a device developed by NASA JPL to measure the speed and direction of ocean surface winds. Simulations performed in order to determine the expected performance of the instrument prior to its launch have indicated that the accuracy of the retrieved wind vectors varies across the swath. In particular the mid-swath accuracy is worse than that of the rest of the swath. This behavior is a general characteristic of scanning pencil beam scatterometers. For SeaWinds, the accuracy of the rest of the swath, and the size of the swath are such that the instrument meets its science requirements despite mid-swath shortcomings. However, by understanding the problem at mid-swath, we can improve the performance there as well, yielding even better coverage and accuracy than was originally intended. The mid-swath performance degradation is due to suboptimal viewing geometry. In particular mid-swath measurements contains measurements from two azimuths, roughly 180 degrees apart. With the standard wind retrieval technique, mid-swath root mean square (RMS) wind direction errors are approximately 30 degrees for low wind speeds (3 - 5.5 m/s) and 20 degrees for moderate to high wind speeds (5.5 - 30 m/s). We discuss the underlying causes of this phenomenon in detail and propose a modification to the wind retrieval technique in order to improve mid-swath performance. By estimating the range of likely wind directions for each measurement cell, one can optimally apply information from neighboring cells in order to reduce random wind direction errors without significantly degrading the resolution of the resultant wind field. In this manner we are able to achieve mid-swath RMS wind direction errors as low as 15 degrees for low winds and 10 degrees for moderate to high winds, while at the same time preserving high resolution structures such as cyclones and fronts. Since our results are based on simulated data, we also outline a procedure for making use of real data to optimize the technique after launch.

Stiles, Bryan↗

NASA NTRS2014

SEU System Analysis: Not Just the Sum of All Parts

Single event upset (SEU) analysis of complex systems is challenging. Currently, system SEU analysis is performed by component level partitioning and then either: the most dominant SEU cross-sections (SEUs) are used in system error rate calculations; or the partition SEUs are summed to eventually obtain a system error rate. In many cases, system error rates are overestimated because these methods generally overlook system level derating factors. The problem with overestimating is that it can cause overdesign and consequently negatively affect the following: cost, schedule, functionality, and validation/verification. The scope of this presentation is to discuss the risks involved with our current scheme of SEU analysis for complex systems; and to provide alternative methods for improvement.

Single Event Upset (SEU) Testing↗

NASA NTRS2013

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

Streamflow Prediction↗

NASA NTRS2012

Polarization and Compressibility of Oblique Kinetic Alfven Waves

Even though solar wind, as a collisionless plasma, is properly described by the kineticMaxwell-Vlasov description, it can be argued that much of our understanding of solar wind observational data comes from an interpretation and numerical modeling which is based on a fluid description of magnetohydrodynamics. In recent years, there has been a significant interest in better understanding the importance of kinetic effects, i.e. the differences between the kinetic and usual fluid descriptions. Here we concentrate on physical properties of oblique kinetic Alfvn waves (KAWs), which are often recognized as one of the key ingredients in the solar wind turbulence cascade. We use three different fluid models with various degrees of complexity and calculate polarization and magnetic compressibility of oblique KAWs (propagation angle q = 88), which we compare to solutions derived from linear kinetic theory. We explore a wide range of possible proton plasma b = [0.1,10.0] and a wide range of length scales krL = [0.001,10.0]. It is shown that the classical isotropic two-fluid model is very compressible in comparison with kinetic theory and that the largest discrepancy occurs at scales larger than the proton gyroscale. We also show that the two-fluid model contains a large error in the polarization of electric field, even at scales krL 1. Furthermore, to understand these discrepancies between the two-fluid model and the kinetic theory, we employ two versions of the Landau fluid model that incorporate linear low-frequency kinetic effects such as Landau damping and finite Larmor radius (FLR) corrections into the fluid description. It is shown that Landau damping significantly reduces the magnetic compressibility and that FLR corrections (i.e. nongyrotropic contributions) are required to correctly capture the polarization.We also show that, in addition to Landau damping, FLR corrections are necessary to accurately describe the damping rate of KAWs. We conclude that kinetic effects are important even at scales which are significantly larger than the proton gyroscale krL 1.

Turbulence↗

NASA NTRS2016

New Developments in FPGA: SEUs and Fail-Safe Strategies from the NASA Goddard Perspective

It has been shown that, when exposed to radiation environments, each Field Programmable Gate Array (FPGA) device has unique error signatures. Subsequently, fail-safe and mitigation strategies will differ per FPGA type. In this session several design approaches for safe systems will be presented. It will also explore the benefits and limitations of several mitigation techniques. The intention of the presentation is to provide information regarding FPGA types, their susceptibilities, and proven fail-safe strategies; so that users can select appropriate mitigation and perform the required trade for system insertion. The presentation will describe three types of FPGA devices and their susceptibilities in radiation environments.

Error Correction and Detection (EDAC)↗

NASA NTRS2018

Spatial Representativeness Error in the Ground-Level Observation Networks for Black Carbon Radiation Absorption

There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error.

Wang, Rong↗

NASA NTRS1999

Mars Rover Navigation Results Using Sun Sensor Heading Determination

This paper describes navigation results obtained in the field tests, where cross-track error was only 6% of distance traveled. Comparison with previous results of other planetary rover systems shows this to be a significant improvement.

rover↗

NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗

NASA NTRSDate not supplied

Methodology for Validation of Unsteady Pressure-Sensitive Paint Measurements using Pressure Transducers

Unsteady Pressure-Sensitive Paint (uPSP) technology provides simultaneously high-frequency and high-spatial resolution surface pressure fluctuation measurements. It has demonstrated value in wind tunnel test facilities to evaluate aerospace vehicle unsteady aerodynamics. The calibration of measurements from digital pixel counts into corresponding physical units relies on both lab-based and in-situ calibration steps, and its validation is both challenging and critical to adoption of the technology. We present a rigorous methodology for direct comparison of uPSP-derived surface pressure measurements to reference pressure transducer data based on a model of the camera-based measurement error, a model of the flow field pressure signal, and their propagation through the uPSP data reduction algorithm. The methodology is demonstrated on data collected in a small-scale, subsonic wind tunnel test as well as data collected from a Space Launch System unsteady aerodynamics characterization test at the NASA Ames Research Center Unitary Plan Wind Tunnel Complex.

Pressure-Sensitive Paint↗

NASA NTRSDate not supplied

Validating uPSP with Transducers

Pressure-Sensitive Paint↗

NASA NTRSDate not supplied

Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions

Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work utilizes an automated grid adaptation tool recently implemented into VULCAN-CFD, and explores this tool’s ability to solve high-speed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flow field parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex highspeed mixing problems using only a fraction of the grid cells required to obtain comparable results using a user-generated structured grid.

grid adaptation↗

NASA NTRSDate not supplied

Simulations of the Modular Axisymmetric Scramjet Test Rig Under Reacting Flow Conditions

Simulations of the Air Force Research Laboratory (AFRL) Modular Axisymmetric Scramjet Test Rig (MASTeR) are presented. MASTeR is a parametric test article capable of investigating various scramjet cavity flameholder designs and fueling strategies with the goal to characterize and optimize flameholding capability. In the current work, three cavity aspect ratios, and two fueling strategies (upstream and in-cavity) with ethylene at a nominal facility pressure and temperature condition are evaluated. The reacting simulations are performed, and the resulting flow characteristics are discussed. For each configuration and fueling strategy, cavity residence time, entrainment rate, and fuel-air equivalence ratio are computed. The MASTeR geometry is defined in Engineering Sketch Pad (ESP) and the simulations use a sketch-to-solution (S2S) automated unstructured grid adaptation tool in VULCAN-CFD. This tool automatically generates a simulation grid from the ESP geometry and systematically adapts it to the numerical solution based on the Hessian error estimate of a specified flow field parameter. Reynolds averaged simulations (RAS) are used with a two-equation linear eddy viscosity and diffusivity model. The resulting database can be compared with the experimental data as those become available, and explored to develop models for cavity performance for scramjet propulsion design applications.

hypersonics↗