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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Polar motion results from GEOS 3 laser ranging

The observability of polar motion from laser range data has been investigated, and the contributions from the dynamical and kinematical effects have been evaluated. Using 2-day arcs with GEOS 3 laser data, simultaneous solutions for pole position components and orbit elements have been obtained for a 2-week interval spanning August 27 to September 10, 1975, using three NASA Goddard Space Flight Center stations located at Washington, D.C., Bermuda, and Grand Turk. The results for the y-component of pole position from this limited data set differenced with the BIH linearly interpolated values yield a mean of 39 cm and a standard deviation of 1.07 m. Consideration of the variance associated with each estimate yields a mean of 20 cm and a standard deviation of 81 cm. The results for the x-component of pole position indicate that the mean value is in fair agreement with the BIH; however, the x-coordinate determination is weaker than the y-coordinate determination due to the distribution of laser sites (all three are between 77 deg W and 65 deg W) which results in greater sensitivity to the data distribution. In addition, the sensitivity of these results to various model parameters is discussed.

Schutz, B. E.↗
NASA NTRS1985

The origin of temporal variance in long-lived trace constituents in the summer stratosphere

Temporal variances in the concentrations of N2O, CF2Cl2, CFCl3 and CH4 in the summer stratosphere at a midlatitude location have been measured by Ehhalt and others. A simple dynamical model is used to argue that these variances are created by irreversible mixing associated with the springtime final stratospheric warming. Tracer perturbations generated during the warming are advected passively in the zonal mean easterlies so that the tracer variance is effectively frozen into the summertime stratosphere. Temperature perturbations, on the other hand, are subject to radiative dissipation; the temperature variance created during the final warming relaxes quickly to an ambient value.

Hess, P. G.↗
NASA NTRS1990

Mesospheric momentum fluxes observed by the MST radar at Poker Flat, Alaska

An analysis of the wave motions observed with the Poker Flat MST radar during the winter, summer, and fall of 1986 is presented. Monthly and daily mean winds, momentum fluxes, and velocity variances are investigated in detail. While several features are in agreement with previous measurements, some significant differences also are found to exist in the observations. Monthly mean horizontal winds between 82 and 89 km have amplitudes of 20-40 m/s westward and 10-25 m/s southward in July and August. In fall and winter, the horizontal winds between 58 and 75 km are weaker and essentially eastward.

Wang, Ding-Yi↗
NASA NTRS1981

Dynamical predictability of monthly means

The concept of predictability which is conditioned by synoptic-scale disturbance instabilities is extended to that of time averages, which are determined by low-frequency planetary wave predictability, in an attempt to determine the theoretical upper limit of dynamical predictability of monthly means for prescribed, nonfluctuating external forcings. Sixty-day integrations of a global general circulation model with nine different initial conditions but identical boundary conditions of sea surface temperatures, snow, sea ice and soil moisture are carried out, where the rms vector wind error between the observed initial conditions is greater than 15 m/sec. It is found that while the variances among the first 30-day means, predicted from mostly different initial conditions, are significantly different from the variances due to random perturbations in the initial conditions, variances for days 31-60 are not so distinguishable. These results suggest that the evolution of long waves remains predictable for between one month and 45 days.

Shukla, J.↗
DOE OSTI2023

Evaluating Retention Index Score Assumptions to Refine GC–MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography–mass spectrometry (GC–MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, in this study, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Experimental Study of the Effect of Start of Injection and Blend Ratio on Single Fuel Reformate RCCI

Abstract A new concept of single fuel reactivity-controlled compression ignition (RCCI) has been proposed through the catalytic partial oxidation (CPOX) reformation of diesel fuel. The reformed fuel mixture is then used as the low reactivity fuel and diesel itself is used as the high reactivity fuel. In this paper, two reformate mixtures from the reformation of diesel were selected for further analysis. Each reformate fuel mixture contained a significant fraction of inert gases (89% and 81%). The effects of the difference in the molar concentrations of the reformate mixtures were studied by experimenting with diesel as the direct injected fuel in RCCI over a varying start of injection timings and different blend ratios (i.e., the fraction of low and high reactivity fuels). The reformate mixture with the lower inert gas concentration had earlier combustion phasing and shorter combustion duration at any given diesel start of injection timing. The higher reactivity separation between reformate mixture and diesel, compared with gasoline and diesel, causes the combustion phasing of reformate-diesel RCCI to be more sensitive to the start of injection timing. The maximum combustion efficiency was found at a CA50 before top dead center (TDC), whereas the maximum thermal efficiency occurs at a CA50 after TDC. The range of energy-based blend ratios in which reformate-diesel RCCI is possible is between 25% and 45%, limited by ringing intensity (RI) at the low limit of blend ratios, and coefficient of variance (COV) of net indicated mean effective pressure (IMEPn) and combustion efficiency at the high limit. Intake boosting becomes necessary due to the oxygen deficiency caused by the low energy density of the reformate mixtures as it displaces intake air.

Engineering↗
NASA NTRS1982

Evaluating the radiance transformation for normalizing Landsat data

A technique is examined for improving the comparability of Landsat multisppectral scanner (MSS) data acquired on different dates. The technique involves conversion of digital brightness counts to relative radiance values measured in energy units (milliwatts per square centimeter-steradian). The statistical data of signature from 23 land cover (or biomass) classifications derived from all three Landsats were compared before and after the radiance normalization. Significant convergence occurred among the data sets for mean spectral values and the variances associated with each of seven major land cover types for MSS bands 4, 5, and 7. Overall, the variance attributed to the sensor component was reduced from 5.39 to 2.69 percent, with the largest decrease occurring in band 4 (14.4 percent to 3.7 percent).

Middleton, E. M.↗
NASA NTRS1987

A general form for the dissipation length scale in turbulent shear flows

It has been found that, for a wide range of turbulent wall-bounded shear flows with mean velocity profile U(y), the scale length L-epsilon determining the dissipation is approximately described in terms of distance from the wall (y), the mean shear (dU/dy), and the variance of the normal component of turbulence (bar u-squared), by the formula: L-epsilon to the -1 power is approximately equal to (A sub B / y) + A sub S (dU/dy / square root of bar u-squared) where L-epsilon = epsilon/(bar u-squared) to the 3/2 power. To match with shear-free boundary layers, A sub B is approximately 0.27, and to match with the log layer, A sub S is approximately 0.46. The shear flows tested here were: boundary layers over a flat plate, sink flow, oscillatory flow, and channel flow. The use of the square root of bar u-squared as a velocity scale minimizes the effects of Reynolds number. However, the formula fails within a distance of order L-epsilon for the regions where dU/dy = 0.

Hunt, J. C. R.↗
NASA NTRS1993

Supersonic flow calculation using a Reynolds-stress and an eddy thermal diffusivity turbulence model

A second-order model for the velocity field and a two-equation model for the temperature field are used to calculate supersonic boundary layers assuming negligible real gas effects. The modeled equations are formulated on the basis of an incompressible assumption and then extended to supersonic flows by invoking Morkovin's hypothesis, which proposes that compressibility effects are completely accounted for by mean density variations alone. In order to calculate the near-wall flow accurately, correction functions are proposed to render the modeled equations asymptotically consistent with the behavior of the exact equations near a wall and, at the same time, display the proper dependence on the molecular Prandtl number. Thus formulated, the near-wall second order turbulence model for heat transfer is applicable to supersonic flows with different Prandtl numbers. The model is validated against flows with different Prandtl numbers and supersonic flows with free-stream Mach numbers as high as 10 and wall temperature ratios as low as 0.3. Among the flow cases considered, the momentum thickness Reynolds number varies from approximately 4,000 to approximately 21,000. Good correlation with measurements of mean velocity, temperature, and its variance is obtained. Discernible improvements in the law-of-the-wall are observed, especially in the range where the big-law applies.

Sommer, T. P.↗
NASA NTRS1994

Investigation of Allan variance for determining noise spectral forms with application to microwave radiometry

An investigation of the Allan variance method as a possible means for characterizing fluctuations in radiometric noise diodes has been performed. The goal is to separate fluctuation components into white noise, flicker noise, and random-walk noise. The primary means is by discrete-time processing, and the study focused primarily on the digital processes involved. Noise satisfying the requirements was generated by direct convolution, fast Fourier transformation (FFT) processing in the time domain, and FFT processing in the frequency domain. Some of the numerous results obtained are presented along with the programs used in the study.

Stanley, William D.↗
NASA NTRS1999

Seasonal-to-Interannual Variability in Antarctic Sea-Ice Dynamics, and Its Impact on Surface Fluxes and Water Mass Production

Strong seasonal and interannual signals in Antarctic bottom-water outflow remain unexplained yet are highly correlated with anomalies in net sea-ice growth in coastal polynyas. The mechanisms responsible for driving salination and replenishment and rejuvenation of the dense shelf "source" waters likely also generate pulses of bottom water outflow. The objective of this research is to investigate time-scales of variability in the dynamics of sea-ice in the Southern Ocean in order to determine the primary sites for production of dense shelf waters. We are using a merged satellite/buoy sea-ice motion data set for the period 1978-present day to compute the dynamics of opening and closing of coastal polynyas over the continental shelf. The Ocean Circulation and Climate Advanced Model (OCCAM) ocean general circulation model with coupled sea-ice dynamics is presently forced using National Center for Environmental Prediction (NCEP) data to simulate fluxes and the salination impact of the ocean shelf regions. This work is relevant in the context of measuring the influence of polar sea-ice dynamics upon polar ocean characteristics, and thereby upon global thermohaline ocean circulation. Interannual variability in simulated net freezing rate in the Southern Weddell Sea is shown for the period 1986-1993. There is a pronounced maximum of ice production in 1988 and minimum in 1991 in response to anomalies in equatorward meridional wind velocity. This follows a similar approximate 8-year interannual cycle in Sea Surface Temperature (SST) and satellite-derived ice-edge anomalies reported elsewhere as the "Antarctic Circumpolar Wave." The amplitude of interannual fluctuations in annual net ice production are about 40% of the mean value, implying significant interannual variance in brine rejection and upper ocean heat loss. Southward anomalies in wind stress induce negative anomalies in open water production, which are observed in passive microwave satellite images. Thus, cycles of enhanced poleward wind stress reduce ice growth by compacting the ice along the coastline and closing open water in leads and polynyas. Model simulations confirm that years of low ice production, such as 1991, coincide with years of lower than normal bottom water outflow. Future plans include the assimilation of satellite ice concentrations and ice drift dynamics to more accurately constrain boundary conditions in the model.

Drinkwater, Mark R.↗
NASA NTRS1999

Measuring Greenland Ice Mass Variation With Gravity Recovery and the Climate Experiment Gravity and GPS

The response of the Greenland ice sheet to climate change could significantly alter sea level. The ice sheet was much thicker at the last glacial maximum. To gain insight into the global change process and the future trend, it is important to evaluate the ice mass variation as a function of time and space. The Gravity Recovery and Climate Experiment (GRACE) mission to fly in 2001 for 5 years will measure gravity changes associated with the current ice variation and the solid earth's response to past variations. Our objective is to assess the separability of different change sources, accuracy and resolution in the mass variation determination by the new gravity data and possible Global Positioning System (GPS) bedrock uplift measurements. We use a reference parameter state that follows a dynamic ice model for current mass variation and a variant of the Tushingham and Peltier ICE-3G deglaciation model for historical deglaciation. The current linear trend is also assumed to have started 5 kyr ago. The Earth model is fixed as preliminary reference Earth model (PREM) with four viscoelastic layers. A discrete Bayesian inverse algorithm is developed employing an isotropic Gaussian a priori covariance function over the ice sheet and time. We use data noise predicted by the University of Texas and JPL for major GRACE error sources. A 2 mm/yr uplift uncertainty is assumed for GPS occupation time of 5 years. We then carry out covariance analysis and inverse simulation using GRACE geoid coefficients up to degree 180 in conjunction with a number of GPS uplift rates. Present-day ice mass variation and historical deglaciation are solved simultaneously over 146 grids of roughly 110 km x 110 km and with 6 time increments of 3 kyr each, along with a common starting epoch of the current trend. For present-day ice thickness change, the covariance analysis using GRACE geoid data alone results in a root mean square (RMS) posterior root variance of 2.6 cm/yr, with fairly large a priori uncertainties in the parameters and a Gaussian correlation length of 350 km. Simulated inverse can successfully recover most features in the reference present-day change. The RMS difference between them over the grids is 2.8 cm/yr. The RMS difference becomes 1.1 cm/yr when both are averaged with a half Gaussian wavelength of 150 km. With a fixed Earth model, GRACE alone can separate the geoid signals due to past and current load fairly well. Shown are the reference geoid signatures of direct and elastic effects of the current trend, the viscoelastic effect of the same trend starting from 5 kyr ago, the Post Glacial Rebound (PGR), and the predicted GRACE geoid error. The difference between the reference and inverse modeled total viscoelastic signatures is also shown. Although past and current ice mass variations are allowed the same spatial scale, their geoid signals have different spatial patterns. GPS data can contribute to the ice mass determination as well. Additional information is contained in the original.

Wu, Xiao-Ping↗
NASA NTRS2000

Long-Term Global Morphology of Gravity Wave Activity Using UARS Data

This report provides a broad outline of the total body of research conducted during the past three years. We report on detailed model studies of the precise way in which infrared limb scanning satellites explicitly detect gravity waves, and use these insights to resolve earlier discrepant zonal mean estimates of gravity wave variances from satellite limb scanners. Detailed analysis of CRISTA temperature fluctuations are outlined, which provide new global information on long-wavelength stratospheric gravity waves generated by mountains, tropical convection and the mid-latitude jet stream-vortex system, as well as interactions higher in the mesosphere with the diurnal tide. The detailed insights gained from analyzing CRISTA data are applied to provide a multiyear analysis of stratospheric mountain waves over the Andes evident in MLS limb-track data. We also demonstrate for the first time that stratospheric temperature data from the CLAES instrument on UARS resolved gravity waves, and we highlight mountain wave information in a subset of these data. Final conclusions and recommendations are set forth, and the present findings are directly related to the original goals of the research contract. A full list of publications that resulted from this research is provided.

Eckermann, Stephen D.↗
NASA NTRS2000

Long-Term Global Morphology of Gravity Wave Activity Using UARS Data

This report provides a broad outline of the total body of research conducted during the past three years. We report on detailed model studies of the precise way in which infrared limb scanning satellites explicitly detect gravity waves, and use these insights to resolve earlier discrepant zonal mean estimates of gravity wave variances from satellite limb scanners. Detailed analysis of CRISTA temperature fluctuations are outlined, which provide new global information on long-wavelength stratospheric gravity waves generated by mountains, tropical convection and the mid-latitude jet stream- vortex system, as well as interactions higher in the mesosphere with the diurnal tide. The detailed insights gained from analyzing CRISTA data are applied to provide a multi- year analysis of stratospheric mountain waves over the Andes evident in MLS limb-track data. We also demonstrate for the first time that stratospheric temperature data from the CLAES instrument on UARS resolved gravity waves, and we highlight mountain wave information in a subset of these data. Final conclusions and recommendations are set forth, and the present findings are directly related to the original goals of the research contract. A full list of publications that resulted from this research is provided.

Eckermann, Stephen D.↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗
DOE OSTI2022

Northern Hemisphere Extratropical Cyclone Activity in the Twentieth Century Reanalysis Version 3 (20CRv3) and Its Relationship with Continental Extreme Temperatures

In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.

20CRv3↗
DOE OSTI2024

Learning Functions Varying along a Central Subspace

Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of an s-Hölder function in $R^D$ which varies along a central subspace of dimension $d$ while $d \ll D$. A direct approximation of $f$ in $R^D$ with an accuracy $\varepsilon$ requires the number of samples in the order of $\varepsilon^{-(2s+D)/s}$. In this paper, we analyze the generalized contour regression (GCR) algorithm for the estimation of the central subspace and use piecewise polynomials for function approximation. GCR is among the best estimators for the central subspace, but its sample complexity is an open question. In this paper, we partially answer this questions by proving that if a variance quantity is exactly known, GCR leads to a mean squared estimation error of $O(n^{-1})$ for the central subspace. The estimation error of this variance quantity is also given in this paper. The mean squared regression error of $f$ is proved to be in the order of $(n/\log n)^{-\frac{2s}{2s+d}}$, where the exponent depends on the dimension of the central subspace instead of the ambient space . This result demonstrates that GCR is effective in learning the low-dimensional central subspace. We also propose a modified GCR with improved efficiency. Here, the convergence rate is validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗