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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Scaling of spanwise length scales in a turbulent boundary layer

The results from an experimental investigation at IIT were combined with Spalart's (1988) Direct Numerical Simulation data base to investigate the scaling of the spanwise length scales of the dominant structures in the near-wall region of a turbulent boundary layer. To achieve this goal, the scaling of the spanwise correlation coefficient between the wall-shear stress and the streamwise velocity, at various heights in the boundary layer, was studied over the Reynolds number range, 670 to 5961. In addition, the scaling of the conditional velocity field associated with high and low wall-shear stress events was examined. The results indicate that the spanwise correlation coefficient scales with inner variables for 'small' spanwise offsets.

Wark, C. E.↗
NASA NTRS1994

Separation of lithospheric, external, and core components of the south polar geomagnetic field at satellite altitudes

We present a new approach to producing scalar Magsat magnetic anomaly maps based on correlation coefficient filtering and the use of almost all of the available orbits. Our method differs from earlier techniques with respect to the following: (1) Passes are selected based on their variance properties rather than planetary indices such as Kp. (2) The core field model is least squares fit to individual passes and subsequently removed instead of substracting the model directly. This technique replaces band pass filtering or polynomial trend removal methods. (3) Each selected pass is sorted geographically and by local time, placed into one of four different altitude bands, and correlation coefficient filtered with the two adjacent passes. The filtering is the second step toward isolating the static lithospheric signal from the more dynamic external field signals. (4) Least squares collocation is used to grid the correlated passes; subsequently, the dawn and dusk maps are also correlation filtered providing another step toward removal of external fields. (5) The four resultant total field maps are continued to a common altitude and again correlation filtered for the static lithospheric anomalies. (6) The filtered results are then averaged together to provide a new total field map of the lithosphere south of 40 deg S latitude. Our total field map differs from previous efforts over the crustal blocks of West Antarctica. We obtained a positive anomaly over Edward VII Peninsula, extending into the Byrd subglacial basin and obtained a negative anomaly over the Ellsworth Mountains and parts of the Byrd subglacial basin. Also, a positive anomaly extending from the Ross Sea to offshore Wilkes Land is present in our map; however, this feature is absent in other maps. Positive anomalies marking the Weddell Sea in previous efforts are not present in our map. Prominent external field anomalies in the quadrant offshore of Wilkes Land are present in all previous efforts; however, these signatures are reduced in our total field map. This map significantly advances efforts for the identification and interpretation of magnetic anomalies of the south polar lithosphere.

Alsdorf, Douglas E.↗
DOE OSTI2020

A Polarimetric Radar Analysis of Ice Microphysical Processes in Melting Layers of Winter Storms Using S-Band Quasi-Vertical Profiles

Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization Z H . QVP ML statistics are examined for two regimes in the ML data: Z H ≥ 20 dB Z and Z H < 20 dB Z . Regions of Z H ≥ 20 dB Z indicate locations of MLs collocated with enhanced differential reflectivity Z DR and reduced copolar correlation coefficient ρ hv , while for Z H < 20 dB Z a well-defined ML is difficult to discern using Z H alone. Evidence of large Z DR up to 4 dB, backscatter differential phase δ up to 8°, and low ρ hv down to 0.80 associated with lower Z H (from -10 to 20 dB Z ) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase K DP and maximum Z H in the ML; these are the first QVP observations of K DP in MLs documented at S band. Negative correlation occurs between minimum ρ hv in the ML and ML depth and between minimum ρ hv in the ML and the corresponding enhancement of Z H (Δ Z H = Z H max - Z H rain ).

Meteorology & Atmospheric Sciences↗
NASA NTRS2020

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability (<10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88° S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of r(exp 2)=−0.32 between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r(exp 2)=0.48, indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind direction, and snow accumulation rates for the overall survey area. We find high variability in surface roughness derived from laser altimetry measurements on length scales smaller than 10 km, sometimes with very distinct and sharp transitions. Some areas also show significant temporal variability over the course of the 3 survey years. Ultimately, there is no statistically significant slope-independent relationship between surface roughness and accumulation rates within our survey area. The observed correspondence between the small-scale temporal and spatial variability in surface roughness and backscatter, as evidenced by Ku-band radar signal strength retrievals, will make it difficult to develop elevation bias corrections for radar altimeter retrieval algorithms.

Michael Studinger↗
NASA NTRS1972

Experimental determination of the turbulence in a liquid rocket combustion chamber

The intensity of turbulence and the Lagrangian correlation coefficient for a liquid rocket combustion chamber were determined experimentally using the tracer gas diffusion method. The results indicate that the turbulent diffusion process can be adequately modeled by the one-dimensional Taylor theory; however, the numerical values show significant disagreement with previously accepted values. The intensity of turbulence is higher by a factor of about two, while the Lagrangian correlation coefficient which was assumed to be unity in the past is much less than unity.

Hara, J.↗
NASA NTRS2011

Simulation of the Indian Summer Monsoon Using Comprehensive Atmosphere-land Interactions, in the Absence of Two-way Air-sea Interactions

Community Land Model version 2 (CLM2) as a comprehensive land surface model and a simple land surface model (SLM) were coupled to an atmospheric climate model to investigate the role of land surface processes in the development and the persistence of the South Asian summer monsoon. Two-way air-sea interactions were not considered in order to identify the reproducibility of the monsoon evolution by the comprehensive land model, which includes more realistic vertical soil moisture structures, vegetation and 2-way atmosphere-land interactions at hourly intervals. In the monsoon development phase (May and June). comprehensive land-surface treatment improves the representation of atmospheric circulations and the resulting convergence/divergence through the improvements in differential heating patterns and surface energy fluxes. Coupling with CLM2 also improves the timing and spatial distribution of rainfall maxima, reducing the seasonal rainfall overestimation by approx.60 % (1.8 mm/d for SLM, 0.7 mm/dI for CLM2). As for the interannual variation of the simulated rainfall, correlation coefficients of the Indian seasonal rainfall with observation increased from 0.21 (SLM) to 0.45 (CLM2). However, in the mature monsoon phase (July to September), coupling with the CLM2 does not exhibit a clear improvement. In contrast to the development phase, latent heat flux is underestimated and sensible heat flux and surface temperature over India are markedly overestimated. In addition, the moisture fluxes do not correlate well with lower-level atmospheric convergence, yielding correlation coefficients and root mean square errors worse than those produced by coupling with the SLM. A more realistic representation of the surface temperature and energy fluxes is needed to achieve an improved simulation for the mature monsoon period.

Monsoon Precipitation↗
NASA NTRS1992

Dependence of Wave-Breaking Statistics on Wind Stress and Wave Development

Incidence of wave breaking for pure wind driven waves has been studied on Lake Washington at wind speeds up to 8 m/s. Video recordings were employed to identify and categorize the breaking events in terms of micro-scale, spilling and plunging breakers. These events were correlated with the magnitude of the wave spectrum measured with a resistance wire wave gauge and band pass filtered between 6 and 10 Hz. An equivalent percentage of breaking crests were found for spilling and plunging events. Wave forcing as measured by wind stress (or friction velocity, u(sub *), squared) and by inverse wave age, u(sub *)/Cp where Cp is the phase velocity of the waves at the peak of the frequency spectrum, were found to be good prerictors of percentage of breaking crests. When combined in a two parameter regression, those two variables gave small standard deviation and had a high correlation coefficient (66 percent). The combination of u(sub *)(exp 2) and u(sub *)/Cp can be understood in physical terms. Furthermore, for the larger values of u(sub *)(exp 2) the dependence of wave braking and wave age was stronger than at the low end of the values u(sub *)(exp 2) and u(sub *)/Cp. Thus, both the level of wave development as determined by inverse wave age, which we may term relative wind effectiveness for wave forcing and the wind forcing on the water surface determine the incidence of wave breaking. Substituting U(sub 10)(sup 3.75) (which is the dependence of whitecap cover found by Monahan and coworkers) an equivalent correlation was found to the prediction by u(sub *)(exp 2). Slightly better standard deviation value and higher correlation coefficient were found by using a Reynolds number as predictor. A two-parameter regression involving u(sub *)(exp 2) and a Reynold's number proposed by Toba and his colleagues which relates u(sub *)(exp 2) and peak wave frequency, improves the correlation even more but is less easy to interpret in physical terms. The equivalent percentage of breaking crests obtained in our previous study was reported at 8.6 percent for a short record obtained at U(sub 10N) of about 6 m/s. Typical values in the current study for similar conditions are 6 percent, which is consistent with the previous study in view of the scatter. In that study we did not have a video recording system, so the observed breaking may include more of the micro-scaic breaking events, and the value, 8.6 percent, is well within the range of highly probable sampling variability.

Katsaros, Kristina B.↗
NASA NTRS1978

Measurement of the turbulence in a gaseous rocket combustion chamber

The intensity of turbulence and the Lagrangian correlation coefficient in a gaseous hydrogen (GH2)-gaseous oxygen (GOX) rocket combustion chamber are studied. A helium tracer gas was injected at an upstream point on the combustion chamber centerline. Samples collected downstream were analyzed for the spreading of the tracer gas concentration. Using Taylor's turbulent diffusion theory and Gosman's numerical method, turbulence parameters were found. In order to evaluate turbulence field inhomogeneity in the axial direction, Taylor's theory was extended. An exponential function was used to approximate the Lagrangian correlation coefficient. It was found that turbulence intensity varies from 15% near the injector to 4% at the nozzle entrance, and that the Lagrangian coefficient rapidly decreases when the dispersion time difference increases.

Tou, P. P.↗
NASA NTRS2012

Comparison of Dst Forecast Models for Intense Geomagnetic Storms

We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.

Geomagnetic Storms↗
DOE OSTI2026

Characterization of the quantum state of top quark pairs produced in proton-proton collisions at $\sqrt{s}$ = 13 TeV using the beam and helicity bases

Measurements of the spin correlation coefficients in the beam basis are presented for top quark-antiquark $(t\bar{t}⁢)$ systems produced in proton-proton collisions at $\sqrt{s}$ = 13 TeV collected by the CMS experiment in 2016–2018, and corresponding to an integrated luminosity of 138 fb −1 . The $t\bar{t}$ system is reconstructed from final states containing an electron or muon and jets. Together with the previously reported results in the helicity basis, these measurements are used to decompose the system into the Bell and spin eigenstates in various kinematic regions. The spin correlation coefficients are also used to evaluate properties of the $t\bar{t}$ quantum state, such as the purity, von Neumann entropy, and entanglement. All results are consistent with standard model predictions.

Hayrapetyan, Aram [Yerevan Physics Institute]↗
NASA NTRS1990

Investigation of Particle Sampling Bias in the Shear Flow Field Downstream of a Backward Facing Step

The flow field about a backward facing step was investigated to determine the characteristics of particle sampling bias in the various flow phenomena. The investigation used the calculation of the velocity:data rate correlation coefficient as a measure of statistical dependence and thus the degree of velocity bias. While the investigation found negligible dependence within the free stream region, increased dependence was found within the boundary and shear layers. Full classic correction techniques over-compensated the data since the dependence was weak, even in the boundary layer and shear regions. The paper emphasizes the necessity to determine the degree of particle sampling bias for each measurement ensemble and not use generalized assumptions to correct the data. Further, it recommends the calculation of the velocity:data rate correlation coefficient become a standard statistical calculation in the analysis of all laser velocimeter data.

Meyers, James F.↗
NASA NTRS1989

Laser transit anemometer software development program

Algorithms were developed for the extraction of two components of mean velocity, standard deviation, and the associated correlation coefficient from laser transit anemometry (LTA) data ensembles. The solution method is based on an assumed two-dimensional Gaussian probability density function (PDF) model of the flow field under investigation. The procedure consists of transforming the data ensembles from the data acquisition domain (consisting of time and angle information) to the velocity space domain (consisting of velocity component information). The mean velocity results are obtained from the data ensemble centroid. Through a least squares fitting of the transformed data to an ellipse representing the intersection of a plane with the PDF, the standard deviations and correlation coefficient are obtained. A data set simulation method is presented to test the data reduction process. Results of using the simulation system with a limited test matrix of input values is also given.

Abbiss, John B.↗
DOE OSTI2023

Arctic Amplification in the Community Earth System Models (CESM1 and CESM2)

We compare the Arctic amplification (AA) produced by the two Community Earth System Models CESM1 and CESM2, members of the CEMIP5 (Coupled Models Intercomparison Project phase 5) and CEMIP6 collections, respectively. We find that the CESM1 model reproduces the recent high values of the AA deduced from the observed temperature much better than the CESM2. The correlation coefficient within the 1970–2012 time period between CESM1-simulated AA and the observed one is 0.47, while the CESM2 simulation leads to an anticorrelation of r = –0.53. Even the more successful model (CESM1) is not able to reproduce recent high AA values of 4–5. The main cause of this failure is the model’s overestimate of the rate of increase in the mean global temperature in years post 1990. When the CESM1 model’s simulated trend of the mean global temperature is replaced in the expression for the AA by the observed temperature trend, the correlation coefficient increases from 0.47 to 0.75. The CESM1 model is among the best north American models in AA simulation while the CESM2 model is among the least successful.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons

This study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar datasets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective up- and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size-intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ~ 0.5), and increases in draft intensity with altitude. A model-observational intercomparison for draft properties (core width, intensity, mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (?x) that range from 4 km to 250 m. It is shown that the simulations performed at ?x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties.

radar, arm, wind profiler, goamazon↗
DOE OSTI2021

Machine Learning Augmented Predictive and Generative Model for Rupture Life in Ferritic and Austenitic Steels

The Larson-Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high temperature applications. However, owing to poor generalizability and dependence on the constant C, which is typically not known a-priori, estimations using the Larson-Miller parameter often result in suboptimal performance for a wide range of materials. At best it is useful in comparing alloys of similar composition. In this work, three machine learning (ML) schemes were developed for rupture life prediction for 9-12% Cr ferritic-martensitic steels and austenitic stainless steels, i.e., a hierarchical model to parameterize LMP using the LMP constant C to compute rupture life, a hierarchical model to parameterize both C and LMP to compute rupture life, and a direct prediction of rupture life. Specifically, we show that the third scheme, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pear-son Correlation Coefficient > 0.9 for 9-12% Cr and > 0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Furthermore, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9-12 % Cr ferritic-martensitic steel and austenitic stainless steel datasets. Finally, based on the predictive and generative model, a reinforcement learning strategy has been proposed to guide experimentalists into designing better heat resistant alloys.

Mamun, Md Osman G.↗
DOE OSTI2020

More Than 40 yr of Potentially Induced Seismicity Close to the San Andreas Fault in San Ardo, Central California

Evidence for fluid–injection–induced seismicity is rare in California hydrocarbon basins, despite widespread injection close to seismically active faults. We investigate a potential case of injection–induced earthquakes associated with San Ardo oilfield operations that began in the early 1950s. The largest potentially induced events occurred in 1955 (M L 5.2) and 1985 (M w 4.5) within ~6 km from the oilfield. We analyze Synthetic Aperture Radar interferometric images acquired by Sentinel–1A/B satellites between 2016 and 2020 and find surface deformation of up to 1.5 cm/yr, indicating pressure–imbalance in parts of the oilfield. Fluid injection in San Ardo is concentrated within highly permeable rocks directly above the granitic basement at a depth of ~800 m. Seismicity predominantly occurs along basement faults at 6–13 km depths. Seismicity and wastewater disposal wells are spatially correlated to the north of the oilfield. Temporal correlations are observed over more than 40 yr with correlation coefficients of up to 0.71 for seismicity within a 24 km distance from the oilfield. Such large distances have not previously been observed in California but are similar to the large spatial footprint of injection in Oklahoma. Furthermore, the San Ardo seismicity shows anomalous clustering with earthquakes consistently occurring at close spatial proximity but long interevent times. Similar clustering has previously been reported in California geothermal fields and may be indicative of seismicity driven by long–term, spatially persistent external forcing.

58 GEOSCIENCES↗
NASA NTRS1983

Assessment of Thematic Mapper band-to-band registration by the block correlation method

Rectangular blocks of pixels from one band image were statistically correlated against blocks centered on identical pixels from a second band image. The block pairs were shifted in pixel increments both vertically and horizontally with respect to each other and the correlation coefficient to the maximum correlation was taken as the best estimate of registration error for each block pair. For the band combinations of the Arkansas scene studied, the misregistration of TM spectral bands within the noncooled focal plane lie well within the 0.2 pixel target specification. Misregistration between the middle IR bands is well within this specification also. The thermal IR band has an apparent misregistration with TM band 7 of approximately 3 pixels in each direction. The TM band 3 has a misregistration of approximately 0.2 pixel in the across-scan direction and 0.5 pixel in the along-scan direction, with both TM bands 5 and 7.

Card, D. H.↗