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At least 901 records · Page 50

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗

Magnetic rotation of the solar photosphere

Magnetograms made at Mt. Wilson Observatory from January 1967 to May 1982 are crosscorrelated in 34 latitude strips at 1-4-day increments to determine the rotation of magnetic features in the solar photosphere. The data are smoothed by averaging corresponding correlations and calculating rotation from the displacement of the averaged-correlation maximum; the usefulness and validity of this procedure are discussed. No significant time variation or field dependence is found for the period of the observations, at least to the accuracy of the calculated means (variance of from about 2 m/sec at low latitudes to about 10 m/sec near the poles). The rotation function omega at solar latitude phi is shown to be 2.902 0.464 sin sq phi - 0.328 sin to the 4th phi microrad/sec, in agreement with the Mt. Wilson Doppler profile near the poles and with the sunspot determination of Newton and Nunn (1951) at sunspot latitudes, where the Doppler estimate is about 30 m/sec slower.

Snodgrass, H. B.↗

Daytime ClO over McMurdo in September 1987: Altitude profile retrieval accuracy

During the 1987 National Ozone Expedition, mm-wave emission line spectra of the 278.6 GHz rotational stratospheric ClO were observed at McMurdo Station, Antarctica. The results confirm the 1986 discovery of a lower stratospheric layer with approximately 100 times the normal amount of ClO; the 1987 observations, made with a spectrometer bandwidth twice that used in 1986, make possible a more accurate retrieval of the altitude profile of the low altitude component of stratospheric ClO from the pressure broadened line shape, down to approximately 16 km. The accuracy of the altitude profile retrievals is discussed, using the daytime (09:30 to 19:30, local time) data from 20 to 24 September, 1987 as an example. The signal strength averaged over this daytime period is approx. 85 percent of the midday peak value. The rate of ozone depletion implied by the observed ClO densities is also discussed.

Barrett, J.↗

Structure of three-dimensional turbulent boundary layers

The changes that occur in the Reynolds-stress-producing motion when a cross-stream pressure gradient is applied to an initially two-dimensional turbulent flow are discussed. Two examples are used: (1) a temporal simulation of a channel flow with crossflow applied by a spanwise pressure gradient for t is greater than 0; and (2) a spatial simulation of the boundary layer on an infinite swept wing. Evidence examined to date suggests that the structural changes in the two cases are similar, but the mechanisms may be significantly different, even if effects peculiar to the viscous wall region are ignored. The results from (2) are provisional, based on too short a time series for accurate statistical averages to be obtained. We treat turbulence 'statistics' (solutions of the Navier-Stokes equations in three space dimensions and time) in the same way as experiments: both have limitations of accuracy but both are acceptable representations of real fluid flows.

Bradshaw, P.↗

Navier-Stokes prediction of large-amplitude delta-wing roll oscillations characterizing wing rock

High-incidence vortical flow about a 65-deg sweep delta wing undergoing static roll and large-amplitude, high-rate-of-roll oscillations is simulated numerically using the time-dependent, three-dimensional, Reynolds-averaged, Navier-Stokes equations. Turbulent computations are presented for static roll angles up through 42 degrees. The effects of roll angle on the vortex aerodynamics are discussed, and the solution accuracy is evaluated by comparison with experimental data. The effects of grid refinement and zonal boundary condition treatment on solution accuracy are assessed at zero roll angle. Numerical simulation of a forced periodic roll motion is also presented.

Chaderjian, Neal M.↗

CALIPSO Lidar Ratio Retrieval Over the Ocean

We are demonstrating on a few cases the capability of CALIPSO to retrieve the 532 nm lidar ratio over the ocean when CloudSat surface scattering cross section is used as a constraint. We are presenting the algorithm used and comparisons with the column lidar ratio retrieved by the NASA airborne high spectral resolution lidar. For the three cases presented here, the agreement is fairly good. The average CALIPSO 532 nm column lidar ratio bias is 13.7% relative to HSRL, and the relative standard deviation is 13.6%. Considering the natural variability of aerosol microphysical properties, this level of accuracy is significant since the lidar ratio is a good indicator of aerosol types. We are discussing dependencies of the accuracy of retrieved aerosol lidar ratio on atmospheric aerosol homogeneity, lidar signal to noise ratio, and errors in the optical depth retrievals. We are obtaining the best result (bias 7% and standard deviation around 6%) for a nighttime case with a relatively constant lidar ratio (in the vertical) indicative of homogeneous aerosol type

Josset, Damien B.↗

Characterization of the Morphometry of Impact Craters Hosting Polar Deposits in Mercury's North Polar Region

Earth-based radar images dating back two decades show that the floors of some polar craters on Mercury host radar-bright deposits that have been proposed to consist of frozen volatiles. Several hypotheses have been put forth to explain their source, including volcanic outgassing, chemical sputtering, and deposition of exogenous water ice. Calculations show that volatiles are thermally stable in permanently shadowed areas. An earlier study of the depths of north polar craters determined with photoclinometric techniques applied to Mariner 10 images yielded the conclusion that the mean ratio of crater depth d to rim-crest diameter D for craters hosting polar deposits is two-thirds that of the mean ratio for a comparable population of neighboring craters lacking such deposits. This result could be explained by (though doesn't require) the presence of a thick layer of volatiles within the polar deposit-hosting craters. Here we use altimetric profiles and topographic maps obtained by the Mercury Laser Altimeter (MLA) to revisit this analysis. MLA is an instrument on the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft, which has been orbiting Mercury since March 2011. MLA transmits a 1064-nm laser pulse at 8 Hz during MESSENGER's trajectory over Mercury s surface. The MLA illuminates surface areas averaging between 15 m and 100 m in diameter, spaced approx 400 m apart along the spacecraft ground track. The radial precision of individual measurements is <1 m, and the current accuracy with respect to Mercury s center of mass is better than 20 m. As of mid-December 2011, MLA coverage had reached to 15 S and has yielded a comprehensive map of the topography of Mercury s northern hemisphere. The MLA data are used here to quantify the shapes of craters in the north polar region and to avoid the shadowing bias of photoclinometric techniques.

Talpe, Matthieu, J.↗

Frequency Stabilization of DFB Laser Diodes at 1572 nm for Spaceborne Lidar Measurements of CO2

We report a fiber-based, pulsed laser seeder system that rapidly switches among 6 wavelengths across atmospheric carbon dioxide (CO2) absorption line near 1572.3 nm for measurements of global CO2 mixing ratios to 1-ppmv precision. One master DFB laser diode has been frequency-locked to the CO2 line center using a frequency modulation technique, suppressing its peak-to-peak frequency drifts to 0.3 MHz at 0.8 sec averaging time over 72 hours. Four online DFB laser diodes have been offset-locked to the master laser using phase locked loops, with virtually the same sub-MHz absolute accuracy. The 6 lasers were externally modulated and then combined to produce the measurement pulse train.

Numata, Kenji↗

An Examination of the Recent Stability of Ozonesonde Global Network Data

The recent Assessment of Standard Operating Procedures for OzoneSondes (ASOPOS 2.0; WMO/GAW Report #268) addressed questions of homogeneity and long-term stability in global electrochemical concentration cell (ECC) ozone sounding network time series. Among its recommendations was adoption of a standard for evaluating data quality in ozonesonde time series. Total column ozone (TCO) derived from the sondes compared to TCO from Aura’s Ozone Monitoring Instrument (OMI) is a primary quality indicator. Comparisons of sonde ozone with Aura’s Microwave Limb Sounder (MLS) are used to assess the stability of stratospheric ozone. This paper provides a comprehensive examination of global ozonesonde network data stability and accuracy since 2004 in light of the sudden post-2013 TCO “dropoff” of ~3-4% that was reported previously at select stations (Stauffer et al., 2020). Comparisons with Aura OMI TCO averaged across the network of 60 stations are stable within about ±2% over the past 18 years. Sonde TCO has similar stability compared to three other TCO satellite instruments, and the stratospheric ozone measurements average to within ±5% of MLS from 50 to 10 hPa. Thus, sonde data are reliable for trends, but with a caveat applied for a subset of dropoff stations in the tropics and subtropics. The dropoff is associated with only one of two major ECC instrument types. A detailed examination of ECC serial numbers pinpoints the timing of the dropoff. However, we find that overall, ozonesonde data are stable and accurate compared to independent measurements over the past two decades.

Ryan M. Stauffer↗

Accuracy of relative positioning by interferometry with GPS Double-blind test results

MITES (Miniature Interferometer Terminals for Earth Surveying) observations conducted on December 17 and 29, 1980, are analyzed. It is noted that the time span of the observations used on each day was 78 minutes, during which five satellites were always above 20 deg elevation. The observations are analyzed to determine the intersite position vectors by means of the algorithm described by Couselman and Gourevitch (1981). The average of the MITES results from the two days is presented. The rms differences between the two determinations of the components of the three vectors, which were about 65, 92, and 124 m long, were 8 mm for the north, 3 mm for the east, and 6 mm for the vertical. It is concluded that, at least for short distances, relative positioning by interferometry with GPS can be done reliably with subcentimeter accuracy.

Counselman, C. C., III↗

TRMM On-Orbit Performance Re-Accessed After Control Change

The Tropical Rainfall Measuring Mission (TRMM) spacecraft, a joint mission between the U.S. and Japan, launched onboard an HI1 rocket on November 27,1997 and transitioned in August, 2001 from an average operating altitude of 350 kilometers to 402.5 kilometers. Due to problems using the Earth Sensor Assembly (ESA) at the higher altitude, TRMM switched to a backup attitude control mode. Prior to the orbit boost TRMM controlled pitch and roll to the local vertical using ESA measurements while using gyro data to propagate yaw attitude between yaw updates from the Sun sensors. After the orbit boost, a Kalman filter used 3-axis gyro data with Sun sensor and magnetometers to estimate onboard attitude. While originally intended to meet a degraded attitude accuracy of 0.7 degrees, the new control mode met the original 0.2 degree attitude accuracy requirement after improving onboard ephemeris prediction and adjusting the magnetometer calibration onboard. Independent roll attitude checks using a science instrument, the Precipitation Radar (PR) which was built in Japan, provided a novel insight into the pointing performance. The PR data helped identify the pointing errors after the orbit boost, track the performance improvements, and show subtle effects from ephemeris errors and gyro bias errors. It also helped identify average bias trends throughout the mission. Roll errors tracked by the PR from sample orbits pre-boost and post-boost are shown in Figure 1. Prior to the orbit boost the largest attitude errors were due to occasional interference in the ESA. These errors were sometime larger than 0.2 degrees in pitch and roll, but usually less, as estimated from a comprehensive review of the attitude excursions using gyro data. Sudden jumps in the onboard roll show up as spikes in the reported attitude since the control responds within tens of seconds to null the pointing error. The PR estimated roll tracks well with an estimate of the roll history propagated using gyro data. After the orbit boost, the attitude errors shown by the PR roll have a smooth sine-wave type signal because of the way that attitude errors propagate with the use of gyro data. Yaw errors couple at orbit period to roll with '/4 orbit lag. By tracking the amplitude, phase, and bias of the sinusoidal PR roll error signal, it was shown that the average pitch rotation axis tends to be offset from orbit normal in a direction perpendicular to the Sun direction, as shown in Figure 2 for a 200 day period following the orbit boost. This is a result of the higher accuracy and stability of the Sun sensor measurements relative to the magnetometer measurements used in the Kalman filter. In November, 2001 a magnetometer calibration adjustment was uploaded which improved the pointing performance, keeping the roll and yaw amplitudes within about 0.1 degrees. After the boost, onboard ephemeris errors had a direct effect on the pitch pointing, being used to compute the Earth pointing reference frame. Improvements after the orbit boost have kept the the onboard ephemeris errors generally below 20 kilometers. Ephemeris errors have secondary effects on roll and yaw, especially during high beta angle when pitch effects can couple into roll and yaw. This is illustrated in figure 3. The onboard roll bias trends as measured by PR data show correlations with the Kalman filter's gyro bias error. This particularly shows up after yaw turns (every 2 to 4 weeks) as shown in Figure 3, when a slight roll bias is observed while the onboard computed gyro biases settle to new values. As for longer term trends, the PR data shows that the roll bias was influenced by Earth horizon radiance effects prior to the boost, changing values at yaw turns, and indicated a long term drift as shown in Figure 4. After the boost, the bias variations were smaller and showed some possible correlation with solar beta angle, probably due to sun sensor misalignment effects.

Bilanow, Steve↗

TRMM On-Orbit Performance Reassessed After Control Change

The Tropical Rainfall Measuring Mission (TRMM) spacecraft, a joint mission between the U.S. and Japan, launched onboard an H-I1 rocket on November 27, 1997, and transitioned in August, 2001, from an average operating altitude of 350 kilometers to 402.5 kilometers. Due to problems using the Earth Sensor Assembly (ESA) at the higher altitude, TRMM switched to a backup attitude control mode. Prior to the orbit boost TRMM controlled pitch and roll to the local vertical using ESA measurements while using gyro data to propagate yaw attitude between yaw updates from the Sun sensors. After the orbit boost, a Kalman filter used 3-axis gyro data with Sun sensor and magnetometers to estimate onboard attitude. While originally intended to meet a degraded attitude accuracy of 0.7 degrees, the new control mode met the original 0.2 degree attitude accuracy requirement after improving onboard ephemeris prediction and adjusting the magnetometer calibration onboard. Independent roll attitude checks using a science instrument, the Precipitation Radar (PR) which was built in Japan, provided a novel insight into the pointing performance. The PR data helped identify the pointing errors after the orbit boost, track the performance improvements, and show subtle effects from ephemeris errors and gyro bias errors. It also helped identify average bias trends throughout the mission. Roll errors tracked by the PR from sample orbits pre-boost and post-boost are shown in Figure 1. Prior to the orbit boost, the largest attitude errors were due to occasional interference in the ESA. These errors were sometime larger than 0.2 degrees in pitch and roll, but usually less, as estimated from a comprehensive review of the attitude excursions using gyro data. Sudden jumps in the onboard roll show up as spikes in the reported attitude since the control responds within tens of seconds to null the pointing error. The PR estimated roll tracks well with an estimate of the roll history propagated using gyro data. After the orbit boost, the attitude errors shown by the PR roll have a smooth sine-wave type signal because of the way that attitude errors propagate with the use of gyro data. Yaw errors couple at orbit period to roll with 1/4 orbit lag. By tracking the amplitude, phase, and bias of the sinusoidal PR roll error signal, it was shown that the average pitch rotation axis tends to be offset from orbit normal in a direction perpendicular to the Sun direction, as shown in Figure 2 for a 200 day period following the orbit boost. This is a result of the higher accuracy and stability of the Sun sensor measurements relative to the magnetometer measurements used in the Kalman filter. In November, 2001 a magnetometer calibration adjustment was uploaded which improved the pointing performance, keeping the roll and yaw amplitudes within about 0.1 degrees.

Bilanow, Stephen↗

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.

54 ENVIRONMENTAL SCIENCES↗

A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Further, our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

17 WIND ENERGY↗

Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels

Modelling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison with classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North American continent, we observe significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.

58 GEOSCIENCES↗

Neural refinement of sample weights

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

Artificial neural networks↗

Advances in Mixed Precision Algorithms: 2021 Edition

Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. In this report, we highlight some of the most promising and impactful achievements of the last year. Among the highlights we present are: Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock; results and strategies for mixed precision IR using a sparse LU factorization; a mixed precision eigenvalue solver; Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES; compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES; preparing hypre for mixed precision execution; mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×; and detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published.

97 MATHEMATICS AND COMPUTING↗

A proposed lunar orbiting gravity gradiometer experiment.

Analysis of the gravity gradiometer developed by Forward and Bell (1970) suggest that an accuracy, in the range 0.1 to 0.5 EU can be expected in a lunar orbiter application. This accuracy will allow gradient anomalies associated with mascons to be mapped with 1% accuracy and should reveal a great deal of new information about the lunar gravity field. The proposed experiment calls for putting such a gradiometer into a closely circular polar orbit at an average height of about 30 km above the lunar surface. This orbit allows the entire lunar surface to be covered in fourteen days, the gradiometer to be checked twice per revolution and results in successive passes above the lunar surface being spaced at about the resolution limit of about 30 km set both by the satellite altitude and instrumental integration time.

Debra, D. B.↗