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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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Results are presented from a study to determine the commercial applications of the technology developed for ACTS. Two systems are discussed: point-to-point transmission of T1 (1.544 Mb/s) data channels using Very Small Aperature Terminals, and point-to-point transmission of single channel per carrier data from extremely small ground terminals. Satellite communications systems designed for each of these applications are described.
This paper deals with the development and use of scaled-down models in order to predict the structural behavior of large prototypes. The concept is fully described and examples are presented which demonstrate its applicability to beam-plates, plates and cylindrical shells of laminated construction. The concept is based on the use of field equations, which govern the response behavior of both the small model as well as the large prototype. The conditions under which the experimental data of a small model can be used to predict the behavior of a large prototype are called scaling laws or similarity conditions and the term that best describes the process is structural similitude. Moreover, since the term scaling is used to describe the effect of size on strength characteristics of materials, a discussion is included which should clarify the difference between "scaling law" and "size effect". Finally, a historical review of all published work in the broad area of structural similitude is presented for completeness.
To protect tropical forest biodiversity, we need to be able to detect it reliably, cheaply, and at scale. Automated detection of sound producing animals from passively recorded soundscapes via machine-learning approaches is a promising technique towards this goal, but it is constrained by the necessity of large training data sets. Using soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN), we investigate i) the minimum viable training data set size for accurate prediction of call types (‘sonotypes’), and ii) the extent to which data augmentation and transfer learning can overcome the issue of small and imbalanced training data sets. We found that even relatively high sample sizes (>80 per sonotype) lead to mediocre accuracy, which however improved significantly with data augmentation and transfer learning, including at extremely small sample sizes (3 per sonotype), regardless of taxonomic group or call characteristics. Neither transfer learning nor data augmentation alone achieved high accuracy. Our results suggest that transfer learning and data augmentation could make the use of CNNs to classify species’ vocalizations feasible even for small soundscape-based projects with many rare species. Retraining our open-source model requires only basic programming skills which makes it possible for individual conservation initiatives to match their local context, in order to enable more evidence-informed management of biodiversity.
The paper presents the results of rainfall-runoff data analysis for small catchments of the upper Poprad River affected by wind-induced deforestation in November 2004. Before-event and afterevent measured data were compared in order to assess the impact of deforestation on hydrological regimes. Several characteristics were used including water balance, minimum and maximum runoff, runoff thresholds, number of runoff events, selected characteristics of events, runoff coefficients, and flashiness indices. Despite increased spring runoff minima, which in one catchment (Velick Creek) exceeded previously observed values after deforestation took place, it can be generally concluded that the impact of the deforestation was not clearly manifested in the analyzed hydrological data.
Small meteorological rocketsondes providing temperature data have beam used for comparison with, and validation of measurements from satellite-borne instruments. A significant number of rocket-borne falling spheres were launched in conjunction with the Upper Atmosphere Research Satellite (UARS) for validation of the Halogen Occultation Experiment (HALOE), High Resolution Doppler Interferometer (HRDI), and the Microwave Limb Sounder (MLS) instruments. Upper stratosphere and mesosphere temperatures measured with these instruments on UARS are compared with inflatable spheres launched from Wallops Island (1992-1999), Brazil (1994), Hawaii (1992), Norway (1992), and Sweden (1993 and 1996). Time and space differences varied between the satellite measurement and the rocketsonde launch, for example HALOE overpasses occurred within 5 days and in some cases there were spatial differences of up to 30 degrees longitude. Validation measurements of the HRDI instrument occurred at Wallops Island when it passed within 20 minutes and 330 kilometers of the launch site. Because of discontinuity in the falling sphere drag coefficients when fall speed neared MACH 1 falling sphere temperatures near 70 kilometers attitude are biased toward lower temperatures. Availability of improved software and a new atmospheric model have helped to reduce this bias. The validated remote instrument measurements permit a new perspective of atmospheric structure to be formed, not always possible with the limited number of falling sphere measurements. Features of the remote measurement temperature profiles and their possible use to extend the climatological data base at the rocketsonde sites will be discussed.
The amount of data generated and gathered in scientific simulations and data collection applications is continuously growing, putting mounting pressure on storage and bandwidth concerns. A means of reducing such issues is data compression; but, lossless data compression is typically ineffective when applied to floating-point data. Thus, users tend to apply a lossy data compressor, which allows for small deviations from the original data. It is essential to understand how the error from lossy compression impacts the accuracy of the data analytics. Thus, we must analyze not only the compression properties but the error as well. In this paper, we provide a statistical analysis of the error caused by ZFP compression, a state-of-the-art, lossy compression algorithm explicitly designed for floating-point data. We show that the error is indeed biased and propose simple modifications to the algorithm to neutralize the bias and further reduce the resulting error.
An analysis of the microwave scanning beam landing system (MSBLS) ground station to orbiter radio frequency (RF) link was made to determine if the expected signal levels will be compatible with orbiter receiver capabilities. Of primary interest was whether or not loss of data will occur due to interference caused by the orbiter 101 nose boom which provides additional air data during the approach and landing test. The results of the analysis indicate that a small amount of data loss may occur due to the proximity of the MSBLS antennas and the nose boom. Tabulated data of antenna radiation patterns are given.
The impact of satellite data is found to vary considerably from one map to another and from one geographic region to another. However, the impact of satellite data is generally small in comparison with forecast error. On the average, over land areas (where good conventional data permit accurate verification), the impact of satellite data on 48-hr forecasts of 500-mb height is seen to be neutral. Indirect numerical evidence is found, however, that satellite data has a marked positive impact in the lower and middle troposphere in ocean regions, especially those regions where conventional data are sparse. The impact over oceans is found to be especially noticeable in maps depicting the mean differences between the satellite and the conventional-data-only maps averaged over the period of the data set (January 1-14, 1980), both for analyses and for forecasts to 48 hours.
The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.
This investigation was carried out by the National Advisory Committee for Aeronautics at the request of the Army Air Service to provide data concerning the small angular oscillations of several types of airplanes in steady flight under various atmospheric conditions. The data are of use in the design of bomb sights and other aircraft instruments. The method used consisted in flying the airplane steadily in one direction for at least one minute, while recording the angle of the airplane with the sun by means of a kymograph. The results show that the oscillations differ but little for airplanes of various types, but that the condition of the atmosphere is an important factor. The average angular excursion from the mean in smooth air is 0.8 degrees in pitch, 1.4 degrees in roll, and 0.9 degrees in yaw, without special instruments to aid the pilot in holding steady conditions. In bumpy air the values given above are increased about 50 per cent. (author)
Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.
A recently developed semi-empirical model for predicting the noise generated by conventional-velocity-profile coaxial jets is compared with full-scale flight data and model-scale simulated flight data for high-bypass nozzles. The prediction model has been shown to agree with small-scale static data for primary jet velocities from 215 to 795 m/s for a wide range of area, temperature and velocity ratios between streams. However, there were insufficient model nozzle, simulated flight data available at that time to permit validation of the flight effects prediction. The comparisons presented in this paper demonstrate that the prediction method is also valid in flight.
Microwave imaging data for a small flare with simultaneous hard X-ray spectral observations are compared. The X-ray data suggest that the power-law index delta of the energy distribution of the radiating electrons is 5.3 (thick-target) which differs significantly from the estimate (delta = 1.4) from a homogeneous optically-thin gyrosynchrotron model which fits the radio observations well. In order to reconcile these results, a double power-law energy spectrum is investigated for the energetic electrons in the flare, as assumed by other authors: the power law is steep at low energies and much flatter at the higher energies which produce the bulk of the microwaves. The emission of soft photons by the flat tail strongly contributes to the observed hard X-ray range and would flatten the spectrum there. A thin-target model for the X-ray emission is also inconsistent with radio data. An inhomogenous gyrosychrotron model with a number of free parameters and containing an electron distribution given by the thick-target X-ray model could be made to fit the radio data.
We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.
The noise of the Harrier AV8C aircraft in vertical takeoff and landing was measured 100 feet to the side of the aircraft where jet noise dominates. The noise levels were quite high - up to 125 dB overall sound level at 100 feet. The increased noise due to jet impingement on the ground is presented as a function of jet height to diameter ratio. The impingement noise with the aircraft close to the ground was 14 to 17 dB greater than noise from a free jet. Results are compared with small-scale jet impingement data acquired elsewhere. The agreement between small-scale and full-scale noise increase in ground effect is fairly good except with the jet close to the ground. It is proposed that differences in the jet Reynolds numbers and the resultant character of the jets may be partially responsible for the disparity in the full-scale and small-scale jet impingement noise. The difference between single-jet impingement and multiple-jet impingement may also have been responsible for the small-scale and full-scale disagreement.
The current study provides the remote sensing community with important high accuracy laboratory-based BRDF calibration of radiometric tarps. The results illustrate the dependence of tarps' weft and warp threads orientation on BRDF. The study was done at incident angles of 0deg, 10deg, and 30deg; scatter zenith angles from 0deg to 60deg, and scatter azimuth angles of 0deg, 45deg, 90deg, 135deg, and 180deg. The wavelengths were 485nm, 550nm, 633nm and 800nm. The dependence is well defined at all measurement geometries and wavelengths. It can be as high as 8% at 0deg incident angle and 2% at 30deg incident angle. The fitted BRDF data show a very small discrepancy from the measured ones. New data on the forward and backscatter properties of radiometric tarps is reported. The backward scatter is well pronounced for the white samples. The black sample has well pronounced forward scatter. The BRDF characterization of radiometric tarps can be successfully extended to other structured surface fabric samples. The results are NIST traceable.
Laboratory-based bidirectional reflectance distribution functions of radiometric tarp samples used in the vicarious calibration of Earth remote sensing satellite instruments are presented in this paper. The results illustrate the BRDF dependence on the orientation of the tarps weft and warp threads. The study was performed using the GSFC scatterometer at incident zenith angles of 0 deg, 10 deg, and 30 deg; scatter zenith angles from 0 deg. to 60 deg.; and scatter azimuth angles of 0 deg., 45 deg., 90 deg., 135 deg. and 180 deg. The wavelengths were 485nm, 550nm, 633nm and 800nm. The tarp's weft and warp dependence on BRDF is well defined at all measurement geometries and wavelengths. The BRDF difference can be as high as 8% at 0o incident angle and 12% at 30 deg. incident angle. The fitted BRDF data shows a very small discrepancy from the measured ones. New data on the forward and backscatter properties of radiometric tarps are reported. The backward scatter is well pronounced for the white samples. The black sample has well pronounced forward scatter. The provided BRDF characterization of radiometric tarps is an excellent reference for anyone interested in using tarps for radiometric calibrations. The results are NIST traceable.
An experimental investigation was made in the Mach number range from 1.60 to 2.86 to determine the static longitudinal aerodynamic characteristics of close-coupled wing-canard configurations. Three canards, ranging in exposed planform area from 17.5 to 30.0 percent of the wing reference area, were employed in this investigation. The canards were either located in the plane of the wing or in a position 18.5 percent of the wing mean geometric chord above the wing plane. Most data obtained were for a model with a 60 deg leading-edge-sweep wing; however, a small amount of data were obtained for a 44 deg leading-edge-sweep wing. The model utilized two balances to isolate interference effects between wing and canard. In general, it was determined that at angle of attack for all configurations investigated with the canard in the plane of the wing an unfavorable interference exists which causes the additional lift on the canard generated by a canard deflection to be lost on the wing due to an increased downwash at the wing from the canard. Further, this interference decreased somewhat with increasing Mach number. Raising the canard above the plane of the wing also greatly decreased the interference of the canard deflection on the wing lift. However, at Mach 2.86 the presence of the canard in the high position had a greater unfavorable interference effect at high angles of attack than the canard in the wing plane. This interference resulted in the in-plane canard having better trimmed performance at Mach 2.86 for the same center-of-gravity location.