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Results for “statistical selection”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Direct Numerical Simulation of Flow Over a Wall-Mounted Cube with the Nek5000 Spectral Element Code: DNS at Re h = 3900

The direct numerical simulation (DNS) of the canonical wall-mounted unit cube subjected to two distinct incident velocity profiles is performed at a Reynolds number where the bulk flow characteristics are known to become relatively Reynolds number insensitive. The aim of this work is to highlight the sensitivity of such bluff-body flows to mean shear and to provide a representative set of data for such flow scenarios where common turbulence modeling techniques often fail. Simple boundary conditions and a small domain are selected to allow for cost-effective and easy comparisons for model development purposes. In addition to mean velocity profiles, select turbulence statistics are presented in detail. Further, the basic efficacy of eddy viscosity models is evaluated and found to be adequate only for the shear stress components for the bluff-body flows examined here. Other failure mechanisms for RANS are proposed.

42 ENGINEERING↗
DOE OSTI2021

A computational study of turbulent separated flow over a wall-mounted cube at two different Reynolds numbers and incoming velocity profiles

Simulations of the canonical wall-mounted unit cube subjected to two distinct incident velocity profiles is performed at Reynolds numbers (Re) where the bulk flow characteristics are known to become relatively Re-insensitive using both direct numerical simulation (DNS) and wall-resolved large eddy simulation (WRLES). The aim of this work is to highlight the sensitivity of such bluff-body flows to mean shear and to provide a representative set of data for such flow scenarios where common turbulence modeling techniques often fail. Simple boundary conditions are selected to allow for easy comparisons for model development purposes and avoid confounding effects from freestream turbulence. In addition to mean velocity profiles, select turbulence statistics are presented in detail. We find that both mean velocity profiles and mean wake turbulence are Re-insensitive over the range examined here.

42 ENGINEERING↗
DOE OSTI2017

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 1

This report summarizes and analyzes data collected on a first test matrix of 36 low-activity waste (LAW) glass compositions intended to expand the LAW glass composition region over which glass property models are valid. The 36 LAW glass compositions were selected using statistical layered experimental design to explore a new glass composition region that overlaps and expands beyond the previously explored LAW glass composition region. The layered design of 36 LAW glasses consisted of 18 outer-layer glasses, 13 inner-layer glasses, a center glass (two replicates), and a glass previously tested at another laboratory (three replicates). The outer-layer glass compositions include extrema of the glass compositions that extend across and slightly beyond the compositions that are expected to be targeted for production. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, Canister Centerline Cooling with crystal identification, Product Consistency Test response, sulfur solubility, iron redox, and Vapor Hydration Test response. This report documents and discusses the results obtained from this testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

The DECam MAGIC Survey: Spectroscopic Follow-up of the Most Metal-poor Stars in the Distant Milky Way Halo *

In this work, we present high-resolution spectroscopic observations for six metal-poor stars with [Fe/H] < –3 (including one with [Fe/H] < –4), selected using narrowband Ca ii HK photometry from the DECam MAGIC Survey. The spectroscopic data confirm the accuracy of the photometric metallicities and allow for the determination of chemical abundances for 16 elements, from carbon to barium. The program stars have chemical abundances consistent with the [Fe/H] < –3 range. A kinematic/dynamical analysis suggests that all program stars belong to the distant Milky Way halo population (heliocentric distances 35 < d helio /kpc ≲ 55), including three with high-energy orbits that might have been associated with the Magellanic system and one, J0026−5445, having parameters consistent with being a member of the Sagittarius stream. The remaining two stars show kinematics consistent with the Gaia-Sausage/Enceladus dwarf galaxy merger. J0433−5548, with [Fe/H] = –4.12, is a carbon-enhanced ultra metal-poor star, with [C/Fe] = +1.73. This star is believed to be a bona fide second-generation star, and its chemical abundance pattern was compared with yields from metal-free supernova models. Results suggest that J0433−5548 could have been formed from a gas cloud enriched by a single supernova explosion from an ∼11 M ⊙ star in the early Universe. The successful identification of such objects demonstrates the reliability of photometric metallicity estimates, which can be used for target selection and statistical studies of faint targets in the Milky Way and its satellite population. These discoveries illustrate the power of measuring chemical abundances of metal-poor Milky Way halo stars to learn more about early galaxy formation and evolution.

CEMP stars↗
DOE OSTI2015

Physics-based statistical learning approach to mesoscopic model selection

In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. Here, we apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD “training” data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD “test” data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Early-type eclipsing binaries with intermediate orbital periods

We analyze 221 eclipsing binaries (EBs) in the Large Magellanic Cloud with B-type main-sequence (MS) primaries (M{sub 1} ≈ 4–14 M{sub ⊙}) and orbital periods P = 20–50 days that were photometrically monitored by the Optical Gravitational Lensing Experiment. We utilize our three-stage automated pipeline to (1) classify all 221 EBs, (2) fit physical models to the light curves of 130 detached well-defined EBs from which unique parameters can be determined, and (3) recover the intrinsic binary statistics by correcting for selection effects. We uncover two statistically significant trends with age. First, younger EBs tend to reside in dustier environments with larger photometric extinctions, an empirical relation that can be implemented when modeling stellar populations. Second, younger EBs generally have large eccentricities. This demonstrates that massive binaries at moderate orbital periods are born with a Maxwellian “thermal” orbital velocity distribution, which indicates they formed via dynamical interactions. In addition, the age-eccentricity anticorrelation provides a direct constraint for tidal evolution in highly eccentric binaries containing hot MS stars with radiative envelopes. The intrinsic fraction of B-type MS stars with stellar companions q = M{sub 2}/M{sub 1} > 0.2 and orbital periods P = 20–50 days is (7 ± 2)%. We find early-type binaries at P = 20–50 days are weighted significantly toward small mass ratios q ≈ 0.2–0.3, which is different than the results from previous observations of closer binaries with P < 20 days. This indicates that early-type binaries at slightly wider orbital separations have experienced substantially less competitive accretion and coevolution during their formation in the circumbinary disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

ROM-based Subset Selection Algorithm for Efficient Surrogate Modeling

This summary discusses a new methodology to support the construction of surrogate models for computationally expensive models with many input parameters and output responses. In realistic engineering calculations, such as thermal-hydraulics simulation, the cost of the simulation is typically expensive, especially when detailed models are employed to describe system behavior, thereby justifying the need for surrogate models. The cost of surrogate model construction is typically a function of the number of model parameters and the degree of model nonlinearity. For sufficiently complex models, the cost of constructing the surrogate model could be by itself overwhelmingly expensive, thereby diminishing its value. One key requirement of surrogate model construction is the ability to preselect the best parametric form to describe the model behavior over the expected range of parameter variations. This summary presents a new algorithm to help select the best approximation out of a template of functions using recent advances in reduced order modeling (ROM) techniques. The idea of selecting functions out of a template is typically referred to as subset selection in the statistical and applied mathematics community. Different from existing subset selection algorithms, we rely on ROM to help identify the most influential functions for the surrogate model. Previous work has shown that ROM can help reduce the effective dimensionality of the parameter space, which can help reduce the cost of surrogate model construction. In this summary, we adopt a different philosophy, where the fitting functions are treated as pseudo parameters and ROM is applied on the pseudo parameter space, with the result now being a ranked list of the pseudo parameters along with their contribution to the quality of the surrogate model fit. This approach allows the analyst to discard all terms that have negligible impact on the quantity of interest. A relap5 model is employed to demonstrate the application of the proposed methodology. (authors)

42 ENGINEERING↗
DOE OSTI2019

Recommended Nuclear Data for the Production of Selected Therapeutic Radionuclides

Increasing interest in the treatment of human disease by means of targeted radionuclide-based therapies requires an accurate understanding of achievable yields and quantification of realisable degrees of purity. Important studies within a more extensive Coordinated Research Project of the International Atomic Energy Agency (IAEA CRP) have included in-depth assessments and evaluations of all available measured cross-section data for ten nuclear reactions leading to the formation of 131 Cs, 131 Ba, 225 Ac, 225 Ra, 227 Th, 227 Ac, 230 U and 230 Pa for various on-going and proposed therapeutic applications in nuclear medicine. Equivalent studies were also undertaken of possible reactions to produce the 178W /low-spin 178m Ta generator system that has been clinically applied in diagnosis by means of SPECT - these particular cross-section studies and recommendations are included in this paper for completeness. All available measured data have been critically reviewed and assessed to produce statistically consistent datasets. Selected datasets have been fitted by a least-squares method with Padé approximations of variable order to evaluate the production cross sections and corresponding uncertainties. New measurements have been made in some instances, and reported data have been adjusted to accommodate new nuclear decay or monitor reaction data which altered the original reported quantities adversely. Insufficient measured data are available in many cases to permit a reliable evaluation of cross sections, and these instances are discussed. Finally, recommended therapeutic radionuclide production reaction data and their uncertainties are available on the Web page of the IAEA Nuclear Data Section at www-nds.iaea.org/radionuclides/ and also at the IAEA medical portal www-nds.iaea.org/medportal/.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
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↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗
DOE OSTI2021

Scalable multiphoton quantum metrology with neither pre- nor post-selected measurements

The quantum statistical fluctuations of electromagnetic fields establish a limit, known as the shot-noise limit, on the sensitivity of optical measurements performed with classical technologies. However, quantum technologies are not constrained by this shot-noise limit. In this regard, the possibility of using every photon produced by quantum sources of light to estimate small physical parameters, beyond the shot-noise limit, constitutes one of the main goals of quantum optics. Here, we experimentally demonstrate a scalable protocol for quantum-enhanced optical phase estimation across a broad range of phases, with neither pre- nor post-selected measurements. This is achieved through the efficient design of a source of spontaneous parametric downconversion in combination with photon-number-resolving detection. The robustness of two-mode squeezed vacuum states against loss allows us to outperform schemes based on N00N states, in which the loss of a single photon is enough to remove all phase information from a quantum state. In contrast to other schemes that rely on N00N states or conditional measurements, the sensitivity of our technique could be improved through the generation and detection of high-order photon pairs. This unique feature of our protocol makes it scalable. Furthermore, our work is important for quantum technologies that rely on multiphoton interference such as quantum imaging, boson sampling, and quantum networks.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.

25 ENERGY STORAGE↗
DOE OSTI2020

Weak-lensing Analysis of X-Ray-selected XXL Galaxy Groups and Clusters with Subaru HSC Data

We present a weak-lensing analysis of X-ray galaxy groups and clusters selected from the XMM-XXL survey using the first-year data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. Our joint weaklensing and X-ray analysis focuses on 136 spectroscopically confirmed X-ray-selected systems at 0.031 6 z 6 1.033 detected in the 25 deg2 XXL-N region, which largely overlaps with the HSC-XMM field. With high-quality HSC weak-lensing data, we characterize the mass distributions of individual clusters and establish the concentration–mass (c–M) relation for the XXL sample, by accounting for selection bias and statistical effects and marginalizing over the remaining mass calibration uncertainty. We find the mass-trend parameter of the c–M relation to be β = -0.07 ± 0.28 and the normalization to be c200 = 4.8 ± 1.0 (stat) ± 0.8 (syst) at M 200 = 1014h -1M and z = 0.3. We find no statistical evidence for redshift evolution. Our weaklensing results are in excellent agreement with dark-matter-only c–M relations calibrated for recent ΛCDM cosmologies. The level of intrinsic scatter in c200 is constrained as σ(ln c 200 ) < 24% (99.7% CL), which is smaller than predicted for the full population of ΛCDM halos. This is likely caused in part by the X-ray selection bias in terms of the cool-core or relaxation state. We determine the temperature–mass (TX–M 500 ) relation for a subset of 105 XXL clusters that have both measured HSC lensing masses and X-ray temperatures. The resulting TX–M 500 relation is consistent with the self-similar prediction. Our TX–M 500 relation agrees with the XXL DR1 results at group scales but has a slightly steeper mass trend, implying a smaller mass scale in the cluster regime. The overall offset in the TX–M 500 relation is at the ~ 1.5σ level, corresponding to a mean mass offset of (34 ± 20)%. We also provide bias-corrected, weak-lensing-calibrated M 200 and M 500 mass estimates of individual XXL clusters based on their measured X-ray temperatures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Ancient light from young cosmic cities: Physical and observational signatures of galaxy proto-clusters

A growing number of galaxy clusters at z = 1-2 is being discovered as part of deep optical, IR, X-ray, and Sunyaev-Zel'dovich effect surveys. For a complete picture of cluster formation, however, it is important that we also start probing the much earlier epoch, between redshifts of about 2 and 7, during which these clusters and their galaxies first began to form. Because the study of these so-called proto-clusters is currently quite limited by small number statistics, widely varying selection techniques, and many assumptions, we have performed a large systematic study of cluster formation utilizing cosmological simulations. We use the Millennium Simulations to track the evolution of dark matter and galaxies in about 3000 clusters from the earliest times to z = 0. We define an effective radius R{sub e} for proto-clusters and characterize their growth in size and mass with cosmic time. We show that the progenitor regions of galaxy clusters (ranging in mass from ∼10{sup 14} to a few times 10{sup 15} M {sub ☉}) can already be identified in galaxy surveys at very early times (at least up to z ∼ 5), provided that the galaxy overdensities are measured on a sufficiently large scale (R{sub e} ∼ 5-10 Mpc comoving) and with sufficient statistics. We present the overdensities in matter, dark matter halos, and galaxies as functions of present-day cluster mass, redshift, bias, and window size that can be used to interpret the wide range of structures found in real surveys. We also derive the probability that a structure having a galaxy overdensity δ{sub gal}, defined by a set of observational selection criteria, is indeed a proto-cluster, and we show how their z = 0 masses can already be estimated long before virialization. We present overdensity profiles as a function of radius, and we further show how the projected surface overdensities of proto-clusters decrease as the uncertainties in redshift measurements increase. We provide a table of proto-cluster candidates selected from the literature and discuss their properties in light of our simulation predictions. This paper provides the general framework that will allow us to extend the study of cluster formation out to much higher redshifts using the large number of proto-clusters that are expected to be discovered in, e.g., the upcoming HETDEX and Hyper Suprime-Cam surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Behavior of the maximum likelihood in quantum state tomography

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) should not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Homogenization of time series from Portugal and its former colonies for the period from the late 19th to the early 21st century

Here, three homogenization software packages – AnClim, RhTestsV3 and HOMER – have been applied to monthly and annual temperature and pressure series from Portugal and its ex-colonies in Africa and Asia to detect non-climatic breaks. Homogenization tests in both absolute and relative mode were performed with an a posteriori statistical assessment to select the most probable breakpoints. Time series from neighbouring stations as well as reanalyses data were used as reference series. The reanalyses data sets were used due to lack of suitable neighbouring stations and also to test reanalyses as proxy data. A list of probable shifts is produced and will be used to support future global reanalyses. Moreover, temperature and pressure series were adjusted using adjustment factors that were obtained by calculating the difference between certain periods before and after the breaks. The accuracy of the adjusted series was verified subjectively (visually) and objectively using three verification techniques: correlation coefficient, central root-mean-square error and standard deviation. A positive impact of the applied adjustment in terms of all three scores was reflected in a majority of cases.

abrupt breaks↗