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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Use of Polarization Lidar for Investigation of Meteorological Formations

This paper presents the results of theoretical and experimental, investigations of depolarization characteristics of different meteorological formations. Experimental investigations are carried out with a monostatic lidar. The ruby laser radiation is polarized in a vertical plane. The radiation reflected is accepted by a lens system of 150 rom in diameter and a viewing angle of 12' and further it is divided by Wollaston prism into the components polarized orthogonally. In this case the principal plane of the prism is exposed parallel with the laser polarization plane. Investigations show the degree of radiation polarization, reflected from water clouds, to be changed within 1/0.7 (seldom up to 0.6) depending on their density. In most cases a signal reflected from the cloud leading edge is polarized completely. The time shift is observed between polarized and crosspolarized components of a Fignal, reflected from a cloud, depending on the density of a meteorological object. While penetrating into the cloud depth a degree of polarization decreases up to 0.8-0.7, and the character of this decrease is different for various types of clouds. For crystal clouds the shift between the components of the reflected signal is not observed and the magnitude of polarization degree amounts to 0.1/0.3 in comparison with water clouds. The polarization degree of radiation reflected by fog is not less than 0.6, and that in the rains of average intensity (about 5 mm/h) is always about 1. The authors have suggested an algorithm of numerical solution of nonstationary transfer equation in the vector form to forecast the influence of multiple scattering effects on polarization characteristics of the lidar light signal. The method of statistical simulation (Monte-Carlo technique) forms the basis of the algorithm. Numerical estimates obtained for a model of stratocumulus at lambda = 0.6943 microns under boundary conditions close to the conditions of natural experiment being discussed proved to be in a good agreement with the results of observation. Specifically, Fig. 1 shows the profiles of polarization (p) versus depth (L) of the following drop formations: fog (curve 1) with horizontal meteorological visibility of 4 km two stratocumulus at a height of 1100 m with the attenuation factors delta = 0.01m(exp -1) (curve 2) and delta = 0.05m(exp -1) (curve 3). Curve 3 shows the results of numerical estimates and the value of their statistical error.

Balin, Yu. S.↗
DOE OSTI2025

Evaluating Extreme Storm Events in an Ensemble of High‐Resolution Projections

This study uses different downscaling techniques and reference observations to investigate the characteristics of extreme storm events over the conterminous United States in historical and a projected future scenario. While previous studies agree on the projected changes in intensity and frequency of precipitation extremes, there is a lack of consensus regarding how their size will change in response to an increase in radiative forcing. Moreover, the influence of different downscaling techniques on their characteristics has not been thoroughly examined. This study employs an ensemble of high‐resolution projections derived from six CMIP6 GCMs, using dynamical, statistical and artificial intelligence based downscaling techniques and two reference observations. Overall, we find noticeable differences in the size, average depth, and total precipitation volume of these storms among the climate ensembles in the historical period. Despite these differences in the historical period, we find consistent future changes across various ensembles. We find a robust projected increase in storm size during Winter and Spring but a decrease in size during Summer in the East. Nevertheless, irrespective of changes in their size, extreme storms are projected to intensify across all the ensembles and seasons.

Rastogi, Deeksha [Oak Ridge National Laboratory (O↗
NASA NTRS1997

Anterior segment sparing to reduce charged particle radiotherapy complications in uveal melanoma

PURPOSE: The purpose of this investigation is to delineate the risk factors in the development of neovascular glaucoma (NVG) after helium-ion irradiation of uveal melanoma patients and to propose treatment technique that may reduce this risk. METHODS AND MATERIALS: 347 uveal melanoma patients were treated with helium-ions using a single-port treatment technique. Using univariate and multivariate statistics, the NVG complication rate was analyzed according to the percent of anterior chamber in the radiation field, tumor size, tumor location, sex, age, dose, and other risk factors. Several University of California San Francisco-Lawrence Berkeley National Laboratory (LBNL) patients in each size category (medium, large, and extralarge) were retrospectively replanned using two ports instead of a single port. By using appropriate polar and azimuthal gaze angles or by treating patients with two ports, the maximum dose to the anterior segment of the eye can often be reduced. Although a larger volume of anterior chamber may receive a lower dose by using two ports than a single port treatment. We hypothesize that this could reduce the level of complications that result from the irradiation of the anterior chamber of the eye. Dose-volume histograms were calculated for the lens, and compared for the single and two-port techniques. RESULTS: NVG developed in 121 (35%) patients. The risk of NVG peaked between 1 and 2.5 years posttreatment. By univariate and multivariate analysis, the percent of lens in the field was strongly correlated with the development of NVG. Other contributing factors were tumor height, history of diabetes, and vitreous hemorrhage. Dose-volume histogram analysis of single-port vs. two-port techniques demonstrate that for some patients in the medium and large category tumor groups, a significant decrease in dose to the structures in the anterior segment of the eye could have been achieved with the use of two ports. CONCLUSION: The development of NVG after helium-ion irradiation is correlated to the amount of lens, anterior chamber in the treatment field, tumor height, proximity to the fovea, history of diabetes, and the development of vitreous hemorrhage. Although the influence of the higher LET deposition of helium-ions is unclear, this study suggests that by reducing the dose to the anterior segment of the eye may reduce the NVG complications. Based on this retrospective analysis of LBNL patients, we have implemented techniques to reduce the amount of the anterior segment receiving a high dose in our new series of patients treated with protons using the cyclotron at the UC Davis Crocker Nuclear Laboratory (CNL).

NASA Discipline Radiation Health↗
NASA NTRS1987

Study of wind change for the development of loads reduction techniques for the space shuttle

Wind change statistics are analyzed for Vandenberg AFB, California (VAFB) and Kennedy Space Center, Florida (KSC). Means and standard deviations of wind component change and vector wind change modulus within 3-9 and 9-16 km altitude bands are tabulated. The contribution to 3.5 hr wind component change by wind perturbations in various wavelength bands is evaluated. Probability distributions of maximum 3.5 hr wind change in an altitude band are presented and a model for wind change at a specified altitude is tested with data derived from six data bases from VAFB and Santa Monica, California.

Adelfang, S. I.↗
NASA NTRS1985

Evaluation of fault-tolerant system performance by approximate techniques

An approximate method for calculating the statistics of the performance of a fault-tolerant system is developed. An approximate method is necessary because the statistical model of the system behavior is large-scale and the time horizon of interest encompasses many cycles of the Redundancy Management logic. In the development, a compact representation of the necessary information called the v-transform is introduced and discussed. Based upon this representation, an approximation that leads to a very efficient computational procedure is suggested and numerically analyzed. A very brief discussion of other related work is also presented.

Walker, B. K.↗
NASA NTRS1988

Collisional-radiative switching - A powerful technique for converging non-LTE calculations

A very simple technique has been developed to converge statistical equilibrium and model atmospheric calculations in extreme non-LTE conditions when the usual iterative methods fail to converge from an LTE starting model. The proposed technique is based on a smooth transition from a collision-dominated LTE situation to the desired non-LTE conditions in which radiation dominates, at least in the most important transitions. The proposed approach was used to successfully compute stellar models with He abundances of 0.20, 0.30, and 0.50; Teff = 30,000 K, and log g = 2.9.

Hummer, D. G.↗
NASA NTRS1993

Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.

REAL TIME↗
NASA NTRS1993

Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.

LOAD CONTROL↗
NASA NTRS1974

A measurement technique of time-dependent dielectric breakdown in MOS capacitors

The statistical nature of time-dependent dielectric breakdown characteristics in MOS capacitors was evidenced by testing large numbers of capacitors fabricated on single wafers. A multipoint probe and automatic electronic visual display technique are introduced that will yield statistical results which are necessary for the investigation of temperature, electric field, thermal annealing, and radiation effects in the breakdown characteristics, and an interpretation of the physical mechanisms involved. It is shown that capacitors of area greater than 0.002 sq cm may yield worst-case results, and that a multipoint probe of capacitors of smaller sizes can be used to obtain a profile of nonuniformities in the SiO2 films.

Li, S. P.↗
DOE OSTI2025

The microbiologist's guide to metaproteomics

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

bioinformatics↗
DOE OSTI2024

Accelerated Depth Computation for Surface Boxplots with Deep Learning

Functional depth is a well-known technique used to derive descriptive statistics (e.g., median, quartiles, and outliers) for 1D data. Surface boxplots extend this concept to ensembles of images, helping scientists and users identify representative and outlier images. However, the computational time for surface boxplots increases cubically with the number of ensemble members, making it impractical for integration into visualization tools. In this paper, we propose a deep-learning solution for efficient depth prediction and computation of surface boxplots for time-varying ensemble data. Our deep learning framework accurately predicts member depths in a surface boxplot, achieving average speedups of 6X on a CPU and 15X on a GPU for the 2D Red Sea dataset with 50 ensemble members compared to the traditional depth computation algorithm. Our approach achieves at least a 99% level of rank preservation, with order flipping occurring only at pairs with extremely similar depth values that pose no statistical differences. This local flipping does not significantly impact the overall depth order of the ensemble members.

Han, Mengjiao↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2024

Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation (Rev.1)

Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to time and computational resource constraints. This paper introduces a novel approach for expressibility estimation of PQCs using Graph Neural Networks (GNNs). We demonstrate the predictive power of our GNN model with a dataset consisting of 25,000 samples from the noiseless IBM QASM Simulator and 12,000 samples from three distinct noisy quantum backends. The model accurately estimates expressibility, with root mean square errors (RMSE) of 0.05 and 0.06 for the noiseless and noisy backends, respectively. We compare our model’s predictions with reference circuits from Sim et al. and IBM Qiskit’s hardwareefficient ansatz sets to further evaluate our model’s performance. Our experimental evaluation in noiseless and noisy scenarios reveals a close alignment with ground truth expressibility values, highlighting the model’s efficacy. Moreover, our model exhibits promising extrapolation capabilities, predicting expressibility values with low RMSE for out-of-range qubit circuits trained solely on only up to 5-qubit circuit sets. This work thus provides a reliable means of efficiently evaluating the expressibility of diverse PQCs on noiseless simulators and hardware.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1969

Wave propagation in a random medium

A simple technique is used to derive statistical characterizations of the perturbations imposed upon a wave (plane, spherical or beamed) propagating through a random medium. The method is essentially physical rather than mathematical, and is probably equivalent to the Rytov method. The limitations of the method are discussed in some detail; in general they are restrictive only for optical paths longer than a few hundred meters, and for paths at the lower microwave frequencies. Situations treated include arbitrary path geometries, finite transmitting and receiving apertures, and anisotropic media. Results include, in addition to the usual statistical quantities, time-lagged functions, mixed functions involving amplitude and phase fluctuations, angle-of-arrival covariances, frequency covariances, and other higher-order quantities.

Lee, R. W.↗
NASA NTRS1971

A real time programmable data compression system for video data.

Description of the implementation of a data compression system for the real-time operational transmission (through microwave links) of ATS satellite pictures between the command and data acquisition station and a central location for computer processing. The system features the use of general-purpose minicomputers for encoding and decoding; this makes it possible to vary the employed data compression technique and to make simultaneous statistical calculations on the data. Data compression and expansion is accomplished in a manner that does not lower data quality.

Kutz, R. L.↗
NASA NTRS1974

Some after-dinner reflections on system identification

The growth of the field of system identification is discussed along with changes in methodology which have taken place in recent years. The similarity between pattern recognition and system identification is pointed out, involving the modelling in the latter and the feature selection problem in the former. It is stated that once a model is formulated, including the disturbances and measurement errors, the parameter finding can be formulated as a statistical estimation problem. The various techniques and their application are discussed.

Balakrishnan, A. V.↗
NASA NTRS1975

Utilization of satellite data for inventorying prairie ponds and lakes. LANDSAT-1 data were used to discriminate ponds and lakes for waterfowl management

The author has identified the following significant results. The mapping of open water as an indicator of waterfowl habitat quality was carried out by using two different recognition techniques, a single waveband thresholding approach and a multiple waveband approach termed proportion estimation. The single waveband technique has proven simple to implement. Its computer algorithm was rapid and accurately recognized prairie lakes and large ponds. The resultant products of this processing technique were thematic maps and statistical tabulations describing open surface water conditions. The maps served to portray visually the location and frequency of surface water bodies but usually necessitated additional interpretation.

Gilmer, D. S.↗