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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Degradation Modeling and Reliability Assessment of Capacitors

The degradation of capacitors under accelerated stress conditions occur in a monotonic and non-linear fashion. Several efforts have been made to model the degradation behavior of capacitor considering either physics-of-failure models or statistical models and subsequently estimate its reliability and lifetime parameters. But most of these models fail to reflect the physical properties of the degradation path, which varies according to several intrinsic and extrinsic factors. These factors introduce random and temporal uncertainty among the population of capacitors. The gamma stochastic process can model both type of uncertainties among the population of capacitors. In this paper, we model the capacitor degradation by non-homogeneous gamma stochastic process in which both the model parameters (shape and scale) are dependent on stress variables. The model parameters are estimated using the maximum likelihood estimation approach.

42 ENGINEERING↗
DOE OSTI2015

Wind Power Curve Modeling in Simple and Complex Terrain

Our previous work on wind power curve modeling using statistical models focused on a location with a moderately complex terrain in the Altamont Pass region in northern California (CA). The work described here is the follow-up to that work, but at a location with a simple terrain in northern Oklahoma (OK). The goal of the present analysis was to determine the gain in predictive ability afforded by adding information beyond the hub-height wind speed, such as wind speeds at other heights, as well as other atmospheric variables, to the power prediction model at this new location and compare the results to those obtained at the CA site in the previous study. While we reach some of the same conclusions at both sites, many results reported for the CA site do not hold at the OK site. In particular, using the entire vertical profile of wind speeds improves the accuracy of wind power prediction relative to using the hub-height wind speed alone at both sites. However, in contrast to the CA site, the rotor equivalent wind speed (REWS) performs almost as well as the entire profile at the OK site. Another difference is that at the CA site, adding wind veer as a predictor significantly improved the power prediction accuracy. The same was true for that site when air density was added to the model separately instead of using the standard air density adjustment. At the OK site, these additional variables result in no significant benefit for the prediction accuracy.

17 WIND ENERGY↗
DOE OSTI2017

A Hybrid Multi-Scale Model of Crystal Plasticity for Handling Stress Concentrations

Microstructural effects become important at regions of stress concentrators such as notches, cracks and contact surfaces. A multiscale model is presented that efficiently captures microstructural details at such critical regions. The approach is based on a multiresolution mesh that includes an explicit microstructure representation at critical regions where stresses are localized. At regions farther away from the stress concentration, a reduced order model that statistically captures the effect of the microstructure is employed. The statistical model is based on a finite element representation of the orientation distribution function (ODF). As an illustrative example, we have applied the multiscaling method to compute the stress intensity factor K I around the crack tip in a wedge-opening load specimen. The approach is verified with an analytical solution within linear elasticity approximation and is then extended to allow modeling of microstructural effects on crack tip plasticity.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 4: Operational applications of satellite snow cover observations. Colorado Field Test Center

The study was conducted on six watersheds ranging in size from 277 km to 3460 km in the Rio Grande and Arkansas River basins of southwestern Colorado. Six years of satellite data in the period 1973-78 were analyzed and snowcover maps prepared for all available image dates. Seven snowmapping techniques were explored; the photointerpretative method was selected as the most accurate. Three schemes to forecast snowmelt runoff employing satellite snowcover observations were investigated. They included a conceptual hydrologic model, a statistical model, and a graphical method. A reduction of 10% in the current average forecast error is estimated when snowcover data in snowmelt runoff forecasting is shown to be extremely promising. Inability to obtain repetitive coverage due to the 18 day cycle of LANDSAT, the occurrence of cloud cover and slow image delivery are obstacles to the immediate implementation of satellite derived snowcover in operational streamflow forecasting programs.

Shafer, B. A.↗
NASA NTRS1994

Empirical convection models for northward IMF

It is clear that polar cap convection during times of northward Interplanetary Magnetic Field (IMF) is more structured and of lower mean speed than at times of southward IMF. This, coupled with the fact that the polar cap is smaller, means that empirical models are more difficult to construct with certainty. It is also clear that sunward flow deep in the polar cap is often observed, but its connection with the rest of the flow pattern is controversial. At present, empirical models are of three types: 'statistical' models wherein data from different days but with similar IMF conditions are averaged together; 'pattern recognition' models, which are built up by examining individually hundreds of passes to derive a 'typical' pattern which embodies features frequently observed; and 'assimilative' models, which use data of different types and from as many locations as possible, but all taken at the same time, in order to derive a snapshot (or series of snapshots) of the entire pattern. Each type of model has its own difficulties. Statistical models, by their very nature, smooth out flow features (e.g. the convection reversal, and the locus of sunward flow deep in the polar cap) which are not found at precisely the same invariant latitudes and magnetic local times on different days. Pattern recognition models are better at reproducing small-scale features, but the large-scale pattern can be a matter of interpretation. Assimilative models (such as AMIE) hold out the best hope for creating instantaneous, global convection patterns; however, the analysis technique tends to be most irregular (and least reliable) in the regions which are not well covered by in situ data. It appears that, at least at times, a four cell model with sunward flow at the highest and lowest latitudes, and antisunward flow in between, is consistent with the observations. At other times, the observations may be consistent with a two-cell convection pattern, but which includes significant meanders within the polar cap.

Moses, Julie J.↗
NASA NTRS1998

Interactions between CO2 chemoreflexes and arterial baroreflexes

We studied interactions between CO2 chemoreflexes and arterial baroreflexes in 10 supine healthy young men and women. We measured vagal carotid baroreceptor-cardiac reflexes and steady-state fast Fourier transform R-R interval and photoplethysmographic arterial pressure power spectra at three arterial pressure levels (nitroprusside, saline, and phenylephrine infusions) and three end-tidal CO2 levels (3, 4, and 5%, fixed-frequency, large-tidal-volume breathing, CO2 plus O2). Our study supports three principal conclusions. First, although low levels of CO2 chemoreceptor stimulation reduce R-R intervals and R-R interval variability, statistical modeling suggests that this effect is indirect rather than direct and is mediated by reductions of arterial pressure. Second, reductions of R-R intervals during hypocapnia reflect simple shifting of vagally mediated carotid baroreflex responses on the R-R interval axis rather than changes of baroreflex gain, range, or operational point. Third, the influence of CO2 chemoreceptor stimulation on arterial pressure (and, derivatively, on R-R intervals and R-R interval variability) depends critically on baseline arterial pressure levels: chemoreceptor effects are smaller when pressure is low and larger when arterial pressure is high.

NASA Discipline Cardiopulmonary↗
NASA NTRS1991

Evaluation of an efficient statistical transport model for turbulent droplet dispersion in dilute combusting sprays

Evaluation of an efficient statistical transport model for turbulent droplet dispersion is made for a dilute spray of methanol droplets injected into a turbulent, axisymmnetric methane-fuelled diffusion flame burning in stagnant air. In the dispersion model, a computational parcel representing a group of physical particles (droplets) is considered to have a normal probability density function (pdf) in space. The mean is determined by Lagrangian tracking through a sequence of stochastically generated turbulent eddies and the variance is evaluated from a statistical formulation based on the linearized particle equations of motion. The basic validity of this model is established through comparison with available experimental data and with theoretical predictions using a conventional stochastic direct modeling approach. The conclusion of the evaluation is that the proposed dispersion model compares favorably with experimental data and provides a valid technique for simulating turbulent combusting sprays with significant computational savings over conventional methods.

Litchford, Ron J.↗
DOE OSTI2014

Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours

Purpose: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. Methods: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. Results: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. Conclusions: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Here, this work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterative method, a lower-fidelity model, or a projection-based reduced-order model, for example. The proposed statistical model comprises the sum of a deterministic regression-function model and a stochastic noise model. The method constructs the regression-function model by applying regression techniques from machine learning (e.g., support vector regression, artificial neural networks) to map features (i.e., error indicators such as sampled elements of the residual) to a prediction of the approximate-solution error. The method constructs the noise model as a mean-zero Gaussian random variable whose variance is computed as the sample variance of the approximate-solution error on a test set; this variance can be interpreted as the epistemic uncertainty introduced by the approximate solution. This work considers a wide range of feature-engineering methods, data-set-construction techniques, and regression techniques that aim to ensure that (1) the features are cheaply computable, (2) the noise model exhibits low variance (i.e., low epistemic uncertainty introduced), and (3) the regression model generalizes to independent test data. Finally, numerical experiments performed on several computational-mechanics problems and types of approximate solutions demonstrate the ability of the method to generate statistical models of the error that satisfy these criteria and significantly outperform more commonly adopted approaches for error modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Energy dependence of light hypernuclei production in heavy-ion collisions from a coalescence and statistical-thermal model perspective

A comparison of light hypernuclei production, from Ultra-relativistic Quantum Molecular Dynamics (UrQMD)+coalescence and the thermal model, in heavy ion collisions over a wide range of beam energies and system sizes is presented. We find that both approaches provide generally similar results, with differences in specific details. Especially the ratios of hypertriton to Λ are affected by both the source radius Δr of the coalescence procedure as well as canonical effects. On the other hand, the double ratio S 3 is almost independent of canonical effects, which is in contrast to coalescence. Thus, both the beam energy dependence and centrality dependence of S 3 can be used to constrain the hypertriton source radius. To do so the currently available data are not yet sufficient. Elliptic flow is shown to be unaffected by the source size of the nuclei and an almost perfect mass scaling of the elliptic flow is observed. Furthermore, our predictions further suggest that the existence of the H-dibaryon(ΛΛ) seems ruled out by ALICE data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Solidarity of signal of measures for the liquid-gas phase transition in the statistical multifragmentation model

A systematic study of system size, N / Z asymmetry, and fragmenting volume effects as well as the Coulomb effect on experimental measures for the nuclear liquid-gas phase transition and the mechanism of phase transition are carried out in the framework of the statistical multifragmentation model (SMM). The measures examined here are the caloric curve, the specific heat capacity ( C v ), the multiplicity derivative (dM / dT), the moment parameters (M2 and γ2), the fluctuation of maximum fragment charge number (NVZ), the Fisher exponent (τ), and the Zipf's law parameter (ξ). A signal for the first-order phase transition for all the measures is observed. A stronger signal is observed in the system with smaller sizes or with more neutron rich or in smaller fragmenting volumes for the caloric curve, Cv, and dM/dT. The phase-transition temperatures are independent of the system size, N / Z asymmetry, fragmenting volume, and Coulomb force, which indicates these measures give a solid signal for the liquid-gas phase transition in SMM. On the other hand, the phase transition temperature and its behavior of the others slightly depend on the size, the N / Z asymmetry and the volume of the system. A negative heat capacity is observed for systems with A s ≥ 100 without the Coulomb force but disappears with the Coulomb. An instructive picture is given in the charge distributions for the observed first-order phase transition.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Automated Particle Classifier Q3 FY18 Quarterly Report

This is the third quarter of the third and final year of this project. Work during this quarter included completing and submitting the unsupervised statistical model paper for the American Statistical Association journal Statistical Analysis and Data Mining , (SADM), and nearly finishing the other paper on the semi-supervised statistical model. We prepared and presented a talk for NSARD 2018. Two papers were published in NIMA this quarter: the comprehensive paper on performance and statistical learning approaches, and the FPGA implementation paper. The team prepared and submitted to NA-22 a description of followon projects for FY19, in part based on discussions at NSARD with the NA-22 Program Manager.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Statistical generic design of glass and optimization: Selective review on oxide glasses

Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C-P models. Recently, the structure (S)-property (P) statistical modeling has emerged as a complementary approach. The S-P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single-component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C-S-P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S-P models and then transferring the designed structures (genes) to a particular composition using C-S models. This article reviews a set of supporting cases from the previous C-S-P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C-S-P database is presented, enabling the application of P?S?C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.

Network structure↗
NASA NTRS2005

Regression Analysis of Long-Term Profile Ozone Data Set from BUV Instruments

We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9, ll, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUT instrument, which was operational from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established based on comparisons with SSBUV shuttle flight data. Differences between NOAA 11, Nimbus 7 and NOAA 9 profile zonal means are within plus or minus 5% at all levels when averaged over the respective periods of data overlap. NOAA 16 SBUV/2 data have insufficient overlap with NOAA 11, so its calibration is based on pre-flight information. Mean differences over 4 months of overlap are within plus or minus 7%. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. Initial comparisons of coincident matches of N4 BUV and Arosa Umkehr data show mean differences of 0.5 (0.5)% at 30km; 7.5 (0.5)% at 35 km; and 11 (0.7)% at 40 km, where the number in parentheses is the standard error of the mean. In this study, we use the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. We use a standard linear regression model with proxies for the seasonal cycle, solar cycle, QBO, and ozone trend. To account for the non-linearity of stratospheric chlorine levels since the late 1990s, we use a time series of Effective Chlorine, defined as the global average of Chlorine + 50 * Bromine at 1 hPa, as the trend proxy. The Effective Chlorine data are taken from the 3-D Goddard CTM. We will show the latest trend results using this statistical model. In addition, the Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. From ground-based comparisons we will establish an uncertainty range for the Nimbus 4 data. We then extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the characteristics of the residual, defined as the difference between the data and statistical regression fit, during the Nimbus 4 time period and the 1978-2003 period over which the statistical model coefficients were estimated, and present these results.

Stolarski, Richard S.↗