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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Combined MODIS/AMSR-E SST Composites for Regional Weather Applications

Recent applications of a high resolution MODlS composite SST product have clearly shown the importance of developing high-resolution SST data sets for coastal applications and modeling. In general, coupling between the oceans and atmospheres has been closely linked to SST gradients and fronts, indicating a need for high resolution SSTs, specifically in the areas of large gradients associated with coastal regions. Thus an accurate determination of SST gradients has become critical for determining the appropriate air-sea coupling and the influence on ocean modeling. Recent research is focused on improving the accuracy and spatial coverage of the current operational MODIS SST composite product provided by the Short-term Prediction Research and Transition (SPORT) project and distributed to the community. GHRSST-PP MODlS data and microwave AMSR-E data are being combined to produce composite data sets for both the West Coast and East Coast of the United States, including the Gulf of Mexico. The use of 1 km MODIS data has explicit advantages over other SST products including its global coverage and high resolution. The AMSR-E data will reduce the latency of the composites. A strategy for utilizing the error characteristics contained in the GHRSST data has been developed. This strategy will include using the error characteristics directly to calculate weights in the SST composites, uncertainty maps based on the composite biases and RMS errors, and latency products calculated in the compositing process. Recent accomplishments include the development of an enhanced compositing approach based on the error-weighted combination of recent clear MODIS SST values, where the error contributions come from measurement error, potential cloud contamination, and data latency sources. Future plans call for the inclusion of AMSR-E SST values with appropriate weights based upon measurement accuracy, MODIS-AMSR-E SST bias, and latency.

Jedlovec, Gary↗
NASA NTRS2019

Assessing the Impact of Observations in a Multi-Year Reanalysis (MERRA-2)

Operational and quasi-operational weather prediction centers have been routinely assessing the contribution from various observing systems to reducing errors in short-range forecasts for a number of years now. The original technique, Forecast Sensitivity-based Observation Impact (FSOI), involves definition of a forecast error measure and evaluation of sensitivities with respect to changes in the observations that require adjoint operators of both the underlying tangent linear model and corresponding analysis technique. The present work applies FSOI to reanalysis and aims at providing an expanded view of the contribution of various observing systems over nearly 40 years of assimilation. Specifically, this study uses MERRA-2 given that its supporting software includes all ingredients necessary to calculate FSOI. Part of this work shows how the quality of forecasts improves over the course of the reanalysis, and examines forecast sensitivities relevant to FSOI. The assessment here finds, for example, that: conventional observations are a major player in reducing forecast error throughout the 40 years of reanalysis, even when their volume reduces from 45\% in the earlier periods to about 5% in the modern era; satellite radiances, especially microwave instruments are major contributors to error reduction from the early single platform TIROS-N days to the current multi-platform scenario, though their fractional contribution reduces slightly from the early 2000's onward after the increased availability of wind observation from aircraft and atmospheric motion vectors, and the introduction of GPSRO; infrared instruments play a secondary role to microwave but are significant still, with the peculiar result of fractional impacts contribution from modern hyperspectral instruments being roughly similar to those from early infrared instruments. The dependence of results on the chosen error measure is emphasized throughout.

Todling, Ricardo↗
NASA NTRS2019

Assessing the Impact of Observations in a Multi-Year Reanalysis

Operational and quasi-operational weather prediction centers have been routinely assessing the contribution from various observing systems to reducing errors in short-range forecasts for a number of years now. The original technique, Forecast Sensitivity Observation Impact (FSOI), involves definition of a forecast error measure and evaluation of sensitivities with respect to changes in the observing system that require adjoint operators of both the underlying tangent linear model and corresponding analysis technique. The present work applies FSOI to Reanalysis and aims at providing an expanded view of the contribution of various observing systems over nearly 40 years of assimilation. Specifically, this study uses MERRA-2 given that its supporting software includes all ingredients necessary to calculate FSOI. Part of this work shows how the quality of forecasts improves over the course of the reanalysis, and examines forecast sensitivities relevant to FSOI. The assessment here finds, for example, that: conventional observations are a major player in reducing forecast error throughout the 40 years of reanalysis, even when their volume reduces from 45% in the earlier periods to about 5% in the modern era; satellite radiances, especially microwave instruments are major contributors to error reduction from the early single platform TIROS-N days to the current multi-platform scenario; infrared instruments play a secondary role to microwave but are significant still, with the peculiar result of fractional impacts contribution from modern hyperspectral instruments being roughly similar to those from early infrared instruments. The dependence of results on the chosen error measure is emphasized throughout.

Fabio L R Diniz↗
DOE OSTI2023

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg 2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Temperature and pressure effects on capacitance probe cryogenic liquid level measurement accuracy

The inaccuracies of liquid nitrogen and liquid hydrogen level measurements by use of a coaxial capacitance probe were investigated as a function of fluid temperatures and pressures. Significant liquid level measurement errors were found to occur due to the changes in the fluids dielectric constants which develop over the operating temperature and pressure ranges of the cryogenic storage tanks. The level measurement inaccuracies can be reduced by using fluid dielectric correction factors based on measured fluid temperatures and pressures. The errors in the corrected liquid level measurements were estimated based on the reported calibration errors of the temperature and pressure measurement systems. Experimental liquid nitrogen (LN2) and liquid hydrogen (LH2) level measurements were obtained using the calibrated capacitance probe equations and also by the dielectric constant correction factor method. The liquid levels obtained by the capacitance probe for the two methods were compared with the liquid level estimated from the fluid temperature profiles. Results show that the dielectric constant corrected liquid levels agreed within 0.5 percent of the temperature profile estimated liquid level. The uncorrected dielectric constant capacitance liquid level measurements deviated from the temperature profile level by more than 5 percent. This paper identifies the magnitude of liquid level measurement error that can occur for LN2 and LH2 fluids due to temperature and pressure effects on the dielectric constants over the tank storage conditions from 5 to 40 psia. A method of reducing the level measurement errors by using dielectric constant correction factors based on fluid temperature and pressure measurements is derived. The improved accuracy by use of the correction factors is experimentally verified by comparing liquid levels derived from fluid temperature profiles.

Edwards, Lawrence G.↗
DOE OSTI2016

A Comparison of Reduced Order Modeling Techniques Used in Dynamic Substructuring [PowerPoint]

Experimental dynamic substructuring is a means whereby a mathematical model for a substructure can be obtained experimentally and then coupled to a model for the rest of the assembly to predict the response. Recently, various methods have been proposed that use a transmission simulator to overcome sensitivity to measurement errors and to exercise the interface between the substructures; including the Craig-Bampton, Dual Craig-Bampton, and Craig-Mayes methods. This work compares the advantages and disadvantages of these reduced order modeling strategies for two dynamic substructuring problems. The methods are first used on an analytical beam model to validate the methodologies. Then they are used to obtain an experimental model for structure consisting of a cylinder with several components inside connected to the outside case by foam with uncertain properties. This represents an exceedingly difficult structure to model and so experimental substructuring could be an attractive way to obtain a model of the system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Enforcing positivity in intrusive PC-UQ methods for reactive ODE systems

We explore the relation between the development of a non-negligible probability of negative states and the instability of numerical integration of the intrusive Galerkin ordinary differential equation system describing uncertain chemical ignition. To prevent this instability without resorting to either multi-element local polynomial chaos (PC) methods or increasing the order of the PC representation in time, we propose a procedure aimed at modifying the amplitude of the PC modes to bring the probability of negative state values below a user-defined threshold. This modification can be effectively described as a filtering procedure of the spectral PC coefficients, which is applied on-the-fly during the numerical integration when the current value of the probability of negative states exceeds the prescribed threshold. We demonstrate the filtering procedure using a simple model of an ignition process in a batch reactor. This is carried out by comparing different observables and error measures as obtained by non-intrusive Monte Carlo and Gauss-quadrature integration and the filtered intrusive procedure. The filtering procedure has been shown to effectively stabilize divergent intrusive solutions, and also to improve the accuracy of stable intrusive solutions which are close to the stability limits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Binary populations in Milky Way satellite galaxies: Constraints from multi-epoch data in the Carina, Fornax, Sculptor, and Sextans dwarf spheroidal galaxies

We introduce a likelihood analysis of multi-epoch stellar line-of-sight velocities to constrain the binary fractions and binary period distributions of dwarf spheroidal galaxies. This method is applied to multi-epoch data from the Magellan/MMFS survey of the Carina, Fornax, Sculptor, and Sextans dSph galaxies, after applying a model for the measurement errors that accounts for binary orbital motion. We find that the Fornax, Sculptor, and Sextans dSphs are consistent with having binary populations similar to that of Milky Way field binaries to within 68% confidence limits, whereas the Carina dSph is remarkably deficient in binaries with periods less than ∼10 yr. If Carina is assumed to have a period distribution identical to that of the Milky Way field, its best-fit binary fraction is 0.14{sub −0.05}{sup +0.28}, and is constrained to be less than 0.5 at the 90% confidence level; thus it is unlikely to host a binary population identical to that of the Milky Way field. By contrast, the best-fit binary fraction of the combined sample of all four galaxies is 0.46{sub −0.09}{sup +0.13}, consistent with that of Milky Way field binaries. More generally, we infer probability distributions in binary fraction, mean orbital period, and dispersion of periods for each galaxy in the sample. Looking ahead to future surveys, we show that the allowed parameter space of binary fraction and period distribution parameters in dSphs will be narrowed significantly by a large multi-epoch survey. However, there is a degeneracy between the parameters that is unlikely to be broken unless the measurement error is of order ∼0.1 km s{sup –1} or smaller, presently attainable only by a high-resolution spectrograph.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Analytical error bars and RSD for neutron multiplicity counting

This article derives the standard deviation of the moments of neutron multiplicity distributions for both random and (shift register) triggered counting gates by propagating the multinomial distribution of errors in the counting distribution. Exact and approximate analytical formulas for the singles, doubles, and triples error bars are given along with the corresponding RSD (relative standard deviation). These analytical formulas provide a simple way to analyze and predict error bars without needing empirical data or simulations. The comparison of these analytical error bars to actual measurement errors can reveal systematic errors that are not being accounted for in the modeling of the data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1971

Study of automatic flare and decrab guidance and control system for the space shuttle

The automatic landing of an unpowered space shuttle vehicle was studied along with the design of the guidance and flight controls, selection of the ground reference component of the landing system (signals in space), and evaluation of the performance of the overall system. The study was carried out by means of an all-digital simulation which incorporated wind shears, gusts and error models for the hardware components. The performance of the landing system operating in winds and turbulence and with hardware errors was determined by Monte Carlo techniques. It was found that the interactions of turbulence and vehicle characteristics were the limiting factors on system performance. The touchdown errors due to the measurement errors of the ground reference system were less significant. The guidance designed was predictive in the longitudinal direction and fixed path in the lateral direction.

Cockayne, W.↗
NASA NTRS1973

Determination of the temperature of gas mixtures by using laser Raman scattering

A theoretical model for the Raman rotational spectrum of air was formulated, yielding the spectral temperature dependence; and an error analysis of temperature measurement accuracies was performed. The theoretical spectra and the predicted temperature measurement error of + or - 20 K (7 percent) compared well with experimental data. These experimental data consisted of Raman spectra of air obtained for temperatures ranging from 243 to 313 K at a pressure of 1 atmosphere. The use of selected lines resulted in an experimental accuracy of + or - 13 K (-5 percent).

Coney, T. A.↗
NASA NTRS1986

A four-dimensional analysis exactly satisfying equations of motion

A four-dimensional analysis is applied to spectral nonlinear models of the atmosphere. The experiment reveals that the four-dimensional analysis errors are smaller than measurement errors, the method is stable in an assimilation cycle, and an accurate estimate of the velocity field is maintained using only temperature observations. It is concluded that the four-dimensional analyses display rapid initial error growth and therefore are better than ordinary forecasts from observations for only the first 24 hours.

Hoffman, R. N.↗
DOE OSTI2016

TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies

Purpose: To set up a framework combining robust treatment planning with adaptive reoptimization in order to maintain high treatment quality, to respond to interfractional variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. Methods: We propose adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan should be able to handle anticipated systematic and random errors and is applied during the first fractions. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient that receives a dose that does not satisfy specified plan quality criteria, the plan is reoptimized based on these individual measurements using one of three different adaptive strategies. The reoptimized plan is then applied during future fractions until a new scheduled adaptation becomes necessary. In the first adaptive strategy the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust reoptimization. The focus of the second strategy lies on variation of the fraction of the worst scenarios taken into account during robust reoptimization. In the third strategy the uncertainty margins around the target are recalculated with the measured errors. Results: By studying the effect of the three adaptive strategies combined with various adaptation schedules on the same patient population, the group which benefits from adaptation is identified together with the most suitable strategy and schedule. Preliminary computational results indicate when and how best to adapt for the three different strategies. Conclusion: A workflow is presented that provides robust adaptation of the treatment plan throughout the course of treatment and useful measures to identify patients in need for an adaptive treatment strategy.

60 APPLIED LIFE SCIENCES↗