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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the Emergent Constraints of Climate Sensitivity

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO 2 ) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

58 GEOSCIENCES↗
DOE OSTI2017

Twentieth-Century Hydrometeorological Reconstructions to Study the Multidecadal Variations of the Water Cycle Over France

Characterizing and understanding the multidecadal variations of the continental hydrological cycle is a challenging issue given the limitation of observed data sets. In this paper, a new approach to derive twentieth century hydrological reconstructions over France with an hydrological model is presented. The method combines the results of long-term atmospheric reanalyses downscaled with a stochastic statistical method and homogenized station observations to derive the meteorological forcing needed for hydrological modeling. Different methodological choices are tested and evaluated. We show that using homogenized observations to constrain the results of statistical downscaling help to improve the reproduction of precipitation, temperature, and river flows variability. In particular, it corrects some unrealistic long-term trends associated with the atmospheric reanalyses. Observationally constrained reconstructions therefore constitute a valuable data set to study the multidecadal hydrological variations over France. Thanks to these reconstructions, we confirm that the multidecadal variations previously noted in French river flows have mainly a climatic origin. Moreover, we show that multidecadal variations exist in other hydrological variables (evapotranspiration, snow cover, and soil moisture). Depending on the region, the persistence from spring to summer of soil moisture or snow anomalies generated during spring by temperature and precipitation variations may explain river flows variations in summer, when no concomitant climate variations exist.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space

Kelvin probe force microscopy (KPFM) has provided deep insights into the role local electronic, ionic and electrochemical processes play on the global functionality of materials and devices, even down to the atomic scale. Conventional KPFM utilizes heterodyne detection and bias feedback to measure the contact potential difference (CPD) between tip and sample. This measurement paradigm, however, permits only partial recovery of the information encoded in bias- and time-dependent electrostatic interactions between the tip and sample and effectively down-samples the cantilever response to a single measurement of CPD per pixel. This level of detail is insufficient for electroactive materials, devices, or solid-liquid interfaces, where non-linear dielectrics are present or spurious electrostatic events are possible. Here, we simulate and experimentally validate a novel approach for spatially resolved KPFM capable of a full information transfer of the dynamic electric processes occurring between tip and sample. General acquisition mode, or G-Mode, adopts a big data approach utilising high speed detection, compression, and storage of the raw cantilever deflection signal in its entirety at high sampling rates (> 4 MHz), providing a permanent record of the tip trajectory. We develop a range of methodologies for analysing the resultant large multidimensional datasets involving classical, physics-based and information-based approaches. Physics-based analysis of G-Mode KPFM data recovers the parabolic bias dependence of the electrostatic force for each cycle of the excitation voltage, leading to a multidimensional dataset containing spatial and temporal dependence of the CPD and capacitance channels. We use multivariate statistical methods to reduce data volume and separate the complex multidimensional data sets into statistically significant components that can then be mapped onto separate physical mechanisms. Overall, G-Mode KPFM offers a new paradigm to study dynamic electric phenomena in electroactive interfaces as well as offer a promising approach to extend KPFM to solid-liquid interfaces.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗
DOE OSTI2018

A New Method for Interpreting Nonstationary Running Correlations and Its Application to the ENSO‐EAWM Relationship

Abstract We here propose a new statistical method to interpret nonstationary running correlations by decomposing them into a stationary part and a first‐order Taylor expansion approximation for the nonstationary part. Then, this method is applied to investigate the nonstationary behavior of the El Niño–Southern Oscillation (ENSO)‐East Asian winter monsoon (EAWM) relationship, which exhibits prominent multidecadal variations. It is demonstrated that the first‐order approximation of the nonstationary part can be expressed to a large extent by the impact of the nonlinear interaction between the Atlantic Multidecadal Oscillation (AMO) and ENSO (AMO*Niño3.4) on the EAWM. Therefore, the nonstationarity in the ENSO‐EAWM relationship comes predominantly from the impact of an AMO modulation on the ENSO‐EAWM teleconnection via this key nonlinear interaction. This general method can be applied to investigate nonstationary relationships that are often observed between various different climate phenomena.

Geng, Xin↗
DOE OSTI2023

An introduction to the significance of sample size in particle analyses for nuclear forensics and radiological investigations

Particulate isotopic analysis in nuclear forensics has developed rapidly during the past two decades due to technical advances in determining the isotopic composition of individual particles. This paper introduces basic statistical concepts that can be applied by analysts to understand the importance of statistical adequacy when interpretating particle data. While these basic statistical methods provide a useful point-of-entry to particle data analysis, more sophisticated statistical and modeling approaches are needed to extract maximal information from such datasets in the future.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Essential Statistical Concepts

Statistical methods are an essential element to making quality measurements, allowing the organization and interrogation of data in a structured way. This introductory chapter describes key concepts of measurement statistics that are used in many nondestructive assay (NDA) methods and is intended as a primer to the reading list provided in the reference section. (Croft and Burr 2016; Smith 1991; Smith 2013; Taylor 1997; Triola 2017). The textbooks by Triola on elementary statistics (Figure 1), Taylor, and Smith (2013) introduce uncertainty analysis in the physical sciences. Smith (1991) provides a rigorous introduction with examples to correlated variables. These texts collectively offer an excellent foundation for understanding essential statistical concepts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

OzDES reverberation mapping program: Lag recovery reliability for 6-yr C iv analysis

ABSTRACT We present the statistical methods that have been developed to analyse the OzDES reverberation mapping sample. To perform this statistical analysis we have created a suite of customizable simulations that mimic the characteristics of each source in the OzDES sample. These characteristics include: the variability in the photometric and spectroscopic light curves, the measurement uncertainties, and the observational cadence. By simulating the sources in the OzDES sample that contain the C iv emission line, we developed a set of criteria that rank the reliability of a recovered time-lag depending on the agreement between different recovery methods, the magnitude of the uncertainties, and the rate at which false positives were found in the simulations. These criteria were applied to simulated light curves and these results used to estimate the quality of the resulting Radius–Luminosity relation. We grade the results using three quality levels (gold, silver, and bronze). The input slope of the R–L relation was recovered within 1σ for each of the three quality samples, with the gold standard having the lowest dispersion with a recovered a R–L relation slope of 0.454 ± 0.016 with an input slope of 0.47. Future work will apply these methods to the entire OzDES sample of 771 AGN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Verification and validation of residual stresses in composite structures

Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses could result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, the objectives of the presented work are to demonstrate an efficient method for simulating residual stresses in composite parts, as well as the potential value of statistical methods during analyses for which material properties are unknown. Specifically, a simplified residual stress modeling approach is implemented within Sandia National Laboratories’ SIERRA/SolidMechanics code. Concurrent with the model development, bi-material composite structures are designed and manufactured to exhibit significant residual stresses. Then, the presented modeling approach is rigorously verified and validated through simulations of the bi-material composite structures’ manufacturing processes, including a mesh convergence study, sensitivity analysis, and uncertainty quantification. The simulations’ final results show adequate agreement with the experimental measurements, indicating the validity of a simple modeling approach, as well as a necessity for the inclusion of material parameter uncertainty in the final residual stress predictions.

42 ENGINEERING↗
DOE OSTI2020

Subsurface Trend Analysis domains for the northern Gulf of Mexico

Geologic domains for the northern Gulf of Mexico derived using the Subsurface Trend Analysis (STA) method. The domains were postulated using geologic province, lithologic, and structural information and validated using statistical methods. Publication detailing the STA method: Rose, K., Bauer, J.R., and Mark-Moser, M. (2020) Subsurface trend analysis, a multi-variate geospatial approach for subsurface evaluation and uncertainty reduction, Interpretation, vol. 8, issue 1 https://library.seg.org/doi/abs/10.1190/int-2019-0019.1 Detailed discussion of domain formation and analysis: Mark-Moser, M., Miller, R., Bauer, J., Rose, K., and C. Disenhof. 2018, Analysis of Subsurface Reservoir Properties Using a Novel Geospatial Approach, Offshore Gulf of Mexico. NETL-TRS-2018 https://edx.netl.doe.gov/dataset/detailed-analysis-of-geospatial-trends-of-hydrocarbon-accumulations-offshore-gulf-of-mexico

geology↗
DOE OSTI2018

Data-Driver Interpretation of Ultradeep Azimuthal Propagation

We report the interpretation of geological features around the borehole stresses on the search of true earth model parameters. Different from the conventional logging-while-drilling (LWD) technologies, such as wireline induction and propagation resistivity tools, the azimuthal resistivity measurement acquires the ability of directional sensitivity. Within this decade, this type of tools has been greatly enhanced with over hundred feet depth-of-detection (DoD). To resolve the interpretation of geological features within 100 feet from the borehole, the multiple-layer structure must be considered. However, the traditional methods can never handle the highly nonlinear inverse problems especially facing on the complicated earth structure. Meanwhile, no justifiable uncertainty of inverse parameters can be provided. The statistical methods have been adopted to search for the global statistical distribution and uncertainties of earth model parameters. However, the most attempts are still stuck in non-uniqueness of the solutions since the structure of earth model has been predefined, where the number of layers is assumed, which introduces another ambiguity into the model interpretation. This paper develops a general trans-dimensional Bayesian methodology to solve non-linear inverse problem of earth layer resistivity and depth to the layer boundaries, from the interpretation of ultra-deep azimuthal resistivity measurements. The presented work is a data-driven approach to search out the multi-dimensional parameter spaces. Finally, the simulations tell that the algorithm can efficiently solve the earth model interpretation within a hundred feet from borehole, and examine the uncertainty of earth model parameters.

58 GEOSCIENCES↗
DOE OSTI2017

The Spiral Arm Segments of the Galaxy within 3 kpc from the Sun: A Statistical Approach

As can be reasonably expected, upcoming large-scale APOGEE, GAIA, GALAH, LAMOST, and WEAVE stellar spectroscopic surveys will yield rather noisy Galactic distributions of stars. In view of the possibility of employing these surveys, our aim is to present a statistical method to extract information about the spiral structure of the Galaxy from currently available data, and to demonstrate the effectiveness of this method. The model differs from previous works studying how objects are distributed in space in its calculation of the statistical significance of the hypothesis that some of the objects are actually concentrated in a spiral. A statistical analysis of the distribution of cold dust clumps within molecular clouds, H ii regions, Cepheid stars, and open clusters in the nearby Galactic disk within 3 kpc from the Sun is carried out. As an application of the method, we obtain distances between the Sun and the centers of the neighboring Sagittarius arm segment, the Orion arm segment in which the Sun is located, and the Perseus arm segment. Pitch angles of the logarithmic spiral segments and their widths are also estimated. The hypothesis that the collected objects accidentally form spirals is refuted with almost 100% statistical confidence. We show that these four independent distributions of young objects lead to essentially the same results. We also demonstrate that our newly deduced values of the mean distances and pitch angles for the segments are not too far from those found recently by Reid et al. using VLBI-based trigonometric parallaxes of massive star-forming regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hawaii Play Fairway Analysis: Lanai Resistivity and Density 3D Inversion Models

To prepare for its third phase, the Hawaii Play Fairway project conducted groundwater sampling and analyses in ten locations in the Hawaiian islands, magnetotelluric (MT) and gravity surveys, as well as calculations of 3D subsurface stress due to the weight of the rock underlying the topography of the volcano. The subsurface stresses were used to evaluate the potential for fracture-induced permeability. Inversions of the MT and gravity data produce 3D models of resistivity and density, respectively, on Lanai, across Haleakala's SW rift (Maui), and surrounding Mauna Kea (Hawaii Island). The project developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The project then incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. These results were used to identify target sites for exploratory drilling. Spreadsheet information: Each sheet contains data for a particular depth in kilometers. Positive depths are above sea level, and negative below. For more information, go to the Hawaii Groundwater and Geothermal Resources Center website linked in the resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Quantification and prediction of solidification textures under additive manufacturing conditions

Crystallographic textures are a major determinant of the macroscale anisotropic properties of polycrystalline metallic alloys produced in a wide range of additive manufacturing (AM) processes. Here, we introduce a statistical method that can accurately quantify the degree of orientational order of textures despite the large random fluctuations in the orientation of individual grains inherent in AM processes. The method, demonstrated for laser and resolidification of AlSi thin films, extends Z-scoring to a dynamical regime to assess the statistical significance of observed textures compared to randomly generated ones at different stages of solidification. We further show that, combined with phase-field modeling, this method can be used to infer fundamental anisotropic properties of the solid-liquid interface that are essential for texture prediction, and are compared here to the results of atomistic simulations. In addition, phase-field modeling reveals that, even at rapid AM solidification rates, the observed 〈110〉-dominated textures in the AlSi thin films are controlled predominantly by the anisotropy of the interface free-energy and sheds light on the physical mechanism of grain competition. These results significantly enhance both the existing tools for the quantification and prediction of AM crystallographic textures and our basic understanding of their formation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Markov Chain Monte Carlo Predictions of Neutron-rich Lanthanide Properties as a Probe of r -process Dynamics

Lanthanide element signatures are key to understanding many astrophysical observables, from merger kilonova light curves to stellar and solar abundances. To learn about the lanthanide element synthesis that enriched our solar system, we apply the statistical method of Markov Chain Monte Carlo to examine the nuclear masses capable of forming the r-process rare-earth abundance peak. We describe the physical constraints we implement with this statistical approach and demonstrate the use of the parallel chains method to explore the multidimensional parameter space. We apply our procedure to three moderately neutron-rich astrophysical outflows with distinct types of r-process dynamics. We show that the mass solutions found are dependent on outflow conditions and are related to the r-process path. We describe in detail the mechanism behind peak formation in each case. We then compare our mass predictions for neutron-rich neodymium and samarium isotopes to the latest experimental data from the CPT at CARIBU. Here, we find our mass predictions given outflows that undergo an extended (n,γ)⇌(γ,n) equilibrium to be those most compatible with both observational solar abundances and neutron-rich mass measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

analytical models↗