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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defned based on the characteristic spatial variations of a meteorological feld or on the remote circulation response to a known forcing, LSMPs are defned relative to the occurrence of a specifc phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary infuence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to defnitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many diferent defnitions of “extreme” are in use, making it difcult to directly compare diferent studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identifed and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Barlow, Mathew↗
DOE OSTI2023

Statistical methods for design and testing of 3D-printed polymers

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. Here, this review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Verification of statistical method CORN for modeling of microfuel in the case of high grain concentration

Two methods of modeling a double-heterogeneity fuel are studied: the deterministic positioning and the statistical method CORN of the MCU software package. The effect of distribution of microfuel in a pebble bed on the calculation results is studied. The results of verification of the statistical method CORN for the cases of the microfuel concentration up to 170 cm{sup –3} in a pebble bed are presented. The admissibility of homogenization of the microfuel coating with the graphite matrix is studied. The dependence of the reactivity on the relative location of fuel and graphite spheres in a pebble bed is found.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Alternating Conditional Expectations: Introducing a Non‐Parametric Statistical Method to Interpret Long‐Term Greenhouse Gas Flux Measurements Over Semi‐Arid and Wetland Ecosystems

Abstract We explore the potential of using a non‐parametric statistical method called Alternating Conditional Expectations, ACE, to quantify functional relationships in biogeosciences. Here, ACE is used to quantify the non‐linear and multi‐faceted responses of greenhouse gas fluxes to a set of biophysical forcings, when the shapes of those response surfaces are unknown. We evaluated the statistical method over two contrasting ecosystems and two contrasting time steps. One case involved quantifying the biophysical controls of water vapor and carbon dioxide (CO 2 ) fluxes over a semi‐arid oak savanna using daily integrated fluxes. The other case evaluated the responses of CO 2 and methane (CH 4 ) flux measurements to a set of biophysical forcings at a restored tidal wetland using thirty‐minute averages. The statistical model, based on 4 independent variables, explained up over 90% of the variation in daily integrated flux densities of water vapor and net carbon dioxide exchange at the savanna site. This fit was defined by distinct non‐linear responses to such drivers as gross primary production, photosynthetically active radiation, air temperature, vapor pressure deficit and soil moisture. At the tidal wetland site, we evaluated net carbon dioxide and methane fluxes with short‐term measurements to capture the influence of rising and falling tides and seasonality in biological activity. The statistical model defined the shape of the forcing of fluxes due to the roles of carbon exudates, water table depth, oxygen level in the water column, temperature and vegetation status. The statistical fits of the greenhouse gas fluxes were less precise than the savanna case. The fetch varies on a run‐to‐run basis as it is comprised of a heterogeneous mosaic of open water and vegetation. Furthermore, it is difficult to monitor the environmental conditions of the archaea and bacteria in the sediments that produce methane and carbon dioxide.

Environmental Sciences & Ecology↗
DOE OSTI2018

Analysis Code - Data Analysis in 'Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications' (LMSMIPNFA) v. 1.0

R code that performs the analysis of a data set presented in the paper ‘Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications’ by Lewis, J., Zhang, A., Anderson-Cook, C. It provides functions for doing inverse predictions in this setting using several different statistical methods. The data set is a publicly available data set from a historical Plutonium production experiment.

Lewis, John↗
DOE OSTI2024

Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors ( X ) of some underlying causal model producing the observables or responses (Y = g ( X ) + error ). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research in which inverse predictions, along with an assessment of predictive capability, are desired.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.

Climate attribution↗
DOE OSTI2017

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Generalizable Evaluated Approach, Applying Advanced Geospatial Statistical Methods, to Identify High Lead Exposure Locations at Census Tract Scale: Michigan Case Study

BACKGROUND: Despite great progress in reducing environmental lead (Pb) levels, many children in the United States are still being exposed. OBJECTIVE: Our aim was to develop a generalizable approach for systematically identifying, verifying, and analyzing locations with high prevalence of children’s elevated blood Pb levels (EBLLs) and to assess available Pb models/indices as surrogates, using a Michigan case study. METHODS: We obtained ~1:9 million BLL test results of children <6 years of age in Michigan from 2006–2016; we then evaluated them for data representativeness by comparing two percentage EBLL (%EBLL) rates (number of children tested with EBLL divided by both number of children tested and total population). We analyzed %EBLLs across census tracts over three time periods and between two EBLL reference values (≥5 vs. ≥10 μg/dL) to evaluate consistency. Locations with high %EBLLs were identified by a top 20 percentile method and a Getis-Ord Gi* geospatial cluster “hotspot” analysis. For the locations identified, we analyzed convergences with three available Pb exposure models/indices based on old housing and sociodemographics. RESULTS: Analyses of 2014–2016 %EBLL data identified 11 Michigan locations via cluster analysis and 80 additional locations via the top 20 percentile method and their associated census tracts. Data representativeness and consistency were supported by a 0.93 correlation coefficient between the two EBLL rates over 11 y, and a Kappa score of ~0:8 of %EBLL hotspots across the time periods (2014–2016) and reference values. Many EBLL hotspot locations converge with current Pb exposure models/indices; others diverge, suggesting additional Pb sources for targeted interventions. DISCUSSION: This analysis confirmed known Pb hotspot locations and revealed new ones at a finer geographic resolution than previously available, using advanced geospatial statistical methods and mapping/visualization. It also assessed the utility of surrogates in the absence of blood Pb data. This approach could be applied to other states to inform Pb mitigation and prevention efforts. https://doi.org/10.1289/EHP9705

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Determination of a most representative cycle from cylinder pressure ensembles via statistical method using distribution skewness

In internal combustion engine research, cylinder pressure measurements provide valuable information about the underlying thermodynamic and combustion processes, and are typically collected in ensembles of several 100 traces. Although in some particular fields of combustion research all traces are analyzed, in most cases only one trace is studied because analyzing all the traces is impractical due to the large number of collected samples. Instead, an ensemble-averaged pressure trace is commonly calculated and used for analysis. However, this pressure trace is highly smoothed and dynamic information is lost during the averaging process. With the average trace, pressure rise rates are lower and pressure oscillations such as the ones resulting from combustion knock are lost. In this work, a statistical method was developed to determine the “most representative cycle,” which is the cycle from the ensemble that has the pressure trace most representative of the engine operating condition. Eleven characteristic parameters are computed from each pressure trace and probabilistic distributions are obtained for each of the parameters using all the traces in the ensemble. Finally, the most representative cycle is selected by means of a cost function minimization. The benefits of this method are illustrated using experimental data from four very different engine platforms, under four different combustion modes and over a range of operating conditions.

42 ENGINEERING↗
DOE OSTI2015

North American extreme temperature events and related large scale meteorological patterns: A review of statistical methods, dynamics, modeling, and trends

This paper reviews research approaches and open questions regarding data, statistical analyses, dynamics, modeling efforts, and trends in relation to temperature extremes. Our specific focus is upon extreme events of short duration (roughly less than 5 days) that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). Methods used to define extreme events statistics and to identify and connect LSMPs to extreme temperatures are presented. Recent advances in statistical techniques can connect LSMPs to extreme temperatures through appropriately defined covariates that supplements more straightforward analyses. A wide array of LSMPs, ranging from synoptic to planetary scale phenomena, have been implicated as contributors to extreme temperature events. Current knowledge about the physical nature of these contributions and the dynamical mechanisms leading to the implicated LSMPs is incomplete. There is a pressing need for (a) systematic study of the physics of LSMPs life cycles and (b) comprehensive model assessment of LSMP-extreme temperature event linkages and LSMP behavior. Generally, climate models capture the observed heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreaks frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Climate models have been used to investigate past changes and project future trends in extreme temperatures. Overall, modeling studies have identified important mechanisms such as the effects of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs more specifically to understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated so more research is needed to understand the limitations of climate models and improve model skill in simulating extreme temperatures and their associated LSMPs. Furthermore, the paper concludes with unresolved issues and research questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Statistical Methods for Improved Evaluation of Environmental Sample Data quarterly FY18Q2

We will develop an enhanced statistical framework to evaluate environmental swipe samples collected at nuclear facilities with the goal of identifying anomalies associated with potential undeclared activities. Our approach will be based on Bayesian methodology and time series analysis. This overall framework should result in a more cost effective and operationally effective approach to environmental swipe sample analysis and interpretation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Statistical methods for network surveillance

The term network surveillance is defined in general terms and illustrated with many examples. Statistical methodologies that can be used as tools for network surveillance are discussed. Details for 3 illustrative examples that address network security, surveillance for data network failures, and surveillance of email traffic flows are presented. Some open areas of research are identified.

Jeske, Daniel R.↗
DOE OSTI2016

Deep mixing in stellar variability: improved method, statistics, and applications

The preliminary results on deep-mixing manifestations in stellar variability are tested using our improved method and extended data set. We measure the timescales τ {sub m} of the stochastic change in the spectral power of rotational harmonics with numbers m ≤ 3 in the light curves of 1361 main-sequence stars from the Kepler mission archive. We find that the gradient [log(τ{sub 2})−log(τ{sub 1})]/[log(2)−log(1)] has a histogram maximum at −2/3, demonstrating agreement with Kolmogorov’s theory of turbulence and therefore confirming the manifestation of deep mixing. The squared amplitudes of the first and second rotational harmonics, corrected for integral photometry distortion, also show a quasi-Kolmogorov character with spectral index ≈−5/3. Moreover, the reduction of τ {sub 1} and τ {sub 2} to the timescales τ {sub lam1} and τ {sub lam2} of laminar convection in the deep stellar layers reveals the proximity of both τ {sub lam1} and τ {sub lam2} to the turnover time τ {sub MLT} of standard mixing length theory. Considering this result, we use the obtained stellar variability timescales instead of τ {sub MLT} in our analysis of the relation between stellar activity and the Rossby number P/τ {sub MLT}. Comparison of our diagrams with previous results and theoretical expectations shows that best-fit correspondence is achieved for τ {sub lam1}, which can therefore be used as an analog of τ {sub MLT}. This means that the laminar component (giant cells) of stellar turbulent convection indeed plays an important role in the physics of stars. Additionally, we estimate the diffusivity of magnetic elements in stellar photospheres.

79 ASTRONOMY AND ASTROPHYSICS↗