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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Uncertainty quantification for evaluating impacts of caprock and reservoir properties on pressure buildup and ground surface displacement during geological CO2 sequestration

A series of numerical test cases reflecting broad and realistic ranges of geological formation properties was developed to systematically evaluate and compare the impacts of those properties on geomechanical responses to CO2 injection. A coupled hydro-geomechanical subsurface transport simulator, STOMP (Subsurface Transport over Multiple Phases), was adopted to simulate the CO2 migration process and geomechanical behaviors of the surrounding geological formations. A quasi-Monte Carlo sampling method was applied to efficiently sample a high-dimensional parameter space consisting of injection rate and 14 subsurface formation properties, including porosity, permeability, entry pressure, irreducible gas and aqueous saturation, Young’s modulus, and Poisson’s ratio for both reservoir and caprock. Generalized cross-validation and analysis of variance methods were used to quantitatively measure the significance of the 15 input parameters. Reservoir porosity, permeability, and injection rate were found to be among the most significant factors affecting the geomechanical responses to the CO2 injection. We used a quadrature generalized linear model to build a reduced-order model that can estimate the geomechanical response instantly instead of running computationally expensive numerical simulations.

CO2 geological sequestration↗
DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Analysis of overlapping count data

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Additionally, proofs of the paper’s results are also included in the supplemental material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Estimating soil N 2 O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N 2 O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N 2 O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N 2 O emissions (N 2 O m , kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N 2 O drivers. Significantly different N 2 O m were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N 2 O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N 2 O emissions reported based on area (N 2 O m ), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N 2 O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N 2 O m using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Linearized modeling and optimization of shared mooring systems

Shared mooring systems, where adjacent platforms are tethered directly to each other, can reduce anchor quantities and mooring line lengths in a floating array but also introduce new modeling and design challenges. This paper presents and demonstrates a first-order approach to modeling and designing shared mooring systems that simplifies these challenges. We formulate a general, linearized model for the force-displacement response of shared mooring systems, including inter-platform couplings. Using this linearization, we realize significant simplifications to the shared mooring system design problem and propose a corresponding design optimization approach. Finally, we demonstrate the complete approach on a variety of shared-mooring floating wind farm layouts with generic design assumptions and constraints. Some fundamental observations about shared mooring systems can be made from the results. Polygonal array layouts with perpendicular anchor positions appear to maximize shared mooring system efficiency. Also, arrays with greater levels of sharing tend to exhibit larger deviations in offsets between platforms. Overall, the results show how the linearized approach can be applied to efficiently evaluate and systematically optimize preliminary shared mooring system designs.

17 WIND ENERGY↗
DOE OSTI2015

Posterior propriety for hierarchical models with log-likelihoods that have norm bounds

Statisticians often use improper priors to express ignorance or to provide good frequency properties, requiring that posterior propriety be verified. Our paper addresses generalized linear mixed models, GLMMs, when Level I parameters have Normal distributions, with many commonly-used hyperpriors. It provides easy-to-verify sufficient posterior propriety conditions based on dimensions, matrix ranks, and exponentiated norm bounds, ENBs, for the Level I likelihood. Since many familiar likelihoods have ENBs, which is often verifiable via log-concavity and MLE finiteness, our novel use of ENBs permits unification of posterior propriety results and posterior MGF/moment results for many useful Level I distributions, including those commonly used with multilevel generalized linear models, e.g., GLMMs and hierarchical generalized linear models, HGLMs. Furthermore, those who need to verify existence of posterior distributions or of posterior MGFs/moments for a multilevel generalized linear model given a proper or improper multivariate F prior as in Section 1 should find the required results in Sections 1 and 2 and Theorem 3 (GLMMs), Theorem 4 (HGLMs), or Theorem 5 (posterior MGFs/moments).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Projection of temperature-related mortality due to cardiovascular disease in beijing under different climate change, population, and adaptation scenarios

Highlights: • The population change can bring 5 times growth to temperature-related CVD deaths. • Increase of heat-related CVD deaths is higher than decrease of cold-related ones. • The adaptation to climate change may cause more temperature-related CVD deaths. Human health faces unprecedented challenges caused by climate change. Thus, studies of the effect of temperature change on total mortality have been conducted in numerous countries. However, few of those studies focused on temperature-related mortality due to cardiovascular disease (CVD) or considered future population changes and adaptation to climate change. We present herein a projection of temperature-related mortality due to CVD under different climate change, population, and adaptation scenarios in Beijing, a megacity in China. To this end, 19 global circulation models (GCMs), 3 representative concentration pathways (RCPs), 3 socioeconomic pathways, together with generalized linear models and distributed lag non-linear models, were used to project future temperature-related CVD mortality during periods centered around the years 2050 and 2070. The number of temperature-related CVD deaths in Beijing is projected to increase by 3.5–10.2% under different RCP scenarios compared with that during the baseline period. Using the same GCM, the future daily maximum temperatures projected using the RCP2.6, RCP4.5, and RCP8.5 scenarios showed a gradually increasing trend. When population change is considered, the annual rate of increase in temperature-related CVD deaths was up to fivefold greater than that under no-population-change scenarios. The decrease in the number of cold-related deaths did not compensate for the increase in that of heat-related deaths, leading to a general increase in the number of temperature-related deaths due to CVD in Beijing. In addition, adaptation to climate change may enhance rather than ameliorate the effect of climate change, as the increase in cold-related CVD mortality greater than the decrease in heat-related CVD mortality in the adaptation scenarios will result in an increase in the total number of temperature-related CVD mortalities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Effects of Omitting Non-confounding Predictors From General Relative-Risk Models for Binary Outcomes

The effects, in terms of bias and precision, of omitting non-confounding predictive covariates from generalized linear models have been well studied, and it is known that such omission results in attenuation bias but increased precision with logistic regression. However, many epidemiologic risk analyses utilize alternative models that are not based on a linear predictor, and the effect of omitting non-confounding predictive covariates from such models has not been characterized. We employed simulation to study the effects on risk estimation of omitting non-confounding predictive covariates from an excess relative risk (ERR) model and a general additive-multiplicative relative-risk mixture model for binary outcome data in a case-control setting. We also compared the results to the effects with ordinary logistic regression. For these commonly employed alternative relative-risk models, the bias was similar to that with logistic regression when the risk was small. More generally, the bias and standard error of the risk-parameter estimates demonstrated patterns that are similar to those with logistic regression, but with greater magnitude depending on the true value of the risk. The magnitude of bias and standard error had little relation to study size or underlying disease prevalence. Prior conclusions regarding omitted covariates in logistic regression models can be qualitatively applied to the ERR and the general additive-multiplicative relative-risk mixture model without substantial change. Quantitatively, however, these alternative models may have slightly greater omitted-covariate bias, depending on the magnitude of the true risk being estimated.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Semisupervised Classification of Texts Using Particle Learning for Probabilistic Automata

This chapter presents a novel online learning system for classification of email texts based on particle learning for composite mixture models involving probabilistic automata. The composite mixture structure allows specification of a joint probability model for heterogeneous collections of independent variables without requiring complex embeddings via generalized linear models or copula techniques. The chapter is organized as follows. Section 12.2 presents a hierarchical model representation of a class of probabilistic automata and derives the particle learning algorithm for obtaining parameter estimates. Section 12.3 describes a framework for semisupervised text classification problems based on a composite mixture model formulation. Section 12.4 discusses an application of the classifier to a spam detection dataset. Section 12.5 concludes with a discussion of computational considerations and future work.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Changes in aerobic power of men, ages 25-70 yr

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak). The cross-sectional sample consisted of 1,499 healthy men ages 25-70 yr. The 156 men of the longitudinal sample were from the same population and examined twice, the mean time between tests was 4.1 (+/- 1.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill exercise test. The zero-order correlations between VO2peak and %fat (r = -0.62) and SR-PA (r = 0.58) were significantly (P < 0.05) higher that the age correlation (r = -0.45). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.46 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.79) showed that nearly 50% of this cross-sectional decline was due to %fat and SR-PA, adding these lifestyle variables to the multiple regression model reduced the age regression weight to -0.26 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results.

Oxygen Consumption/physiology↗
NASA NTRS2002

Nonlinear Dynamic Models in Advanced Life Support

To facilitate analysis, ALS systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis. Nonlinear dynamics can be fully explored only by computer simulations of models. Unexpected behavior is produced by simple models having only two or three state variables with simple mathematical relations between them. Closed loop feedback delays are a major source of system instability. Exceeding limits on buffer storage or processing rates forces systems to change operating mode. Different equilibrium points may be reached from different initial conditions. Instead of one stable equilibrium point, the system may have several equilibrium points, oscillate at different frequencies, or even behave chaotically, depending on the system inputs and initial conditions. The frequency spectrum of an output oscillation may contain harmonics and the sums and differences of input frequencies, but it may also contain a stable limit cycle oscillation not related to input frequencies. We must investigate the nonlinear dynamic aspects of advanced life support systems to understand and counter undesirable behavior.

Jones, Harry↗
NASA NTRS1996

Changes in aerobic power of women, ages 20-64 yr

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak) of women. The cross-sectional sample consisted of 409 healthy women, ages 20-64 yr. The 43 women of the longitudinal sample were from the same population and examined twice, the mean time between tests was 3.7 (+/-2.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill test. The zero-order correlation of -0.742 between VO2peak and %fat was significantly (P < 0.05) higher then the SR-PA (r = 0.626) and age correlations (r = -0.633). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.537 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.851) showed that adding %fat and SR-PA and their interaction to the regression model reduced the age regression weight of -0.537, to -0.265 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results. These findings are consistent with men's data from the same lab showing that about 50% of the cross-sectional age-related decline in VO2peak was due to %fat and SR-PA.

Aging/physiology↗
NASA NTRS2007

The Local Stellar Velocity Field via Vector Spherical Harmonics

We analyze the local field of stellar tangential velocities for a sample of 42,339 nonbinary Hipparcos stars with accurate parallaxes, using a vector spherical harmonic formalism. We derive simple relations between the parameters of the classical linear model (Ogorodnikov-Milne) of the local systemic field and low-degree terms of the general vector harmonic decomposition. Taking advantage of these relationships, we determine the solar velocity with respect to the local stars of (V(sub X), V(sub Y), V(sub Z)) (10.5, 18.5, 7.3) +/- 0.1 km s(exp -1) not corrected for the asymmetric drift with respect to the local standard of rest. If only stars more distant than 100 pc are considered, the peculiar solar motion is (V(sub X), V(sub Y), V(sub Z)) (9.9, 15.6, 6.9) +/- 0.2 km s(exp -1). The adverse effects of harmonic leakage, which occurs between the reflex solar motion represented by the three electric vector harmonics in the velocity space and higher degree harmonics in the proper-motion space, are eliminated in our analysis by direct subtraction of the reflex solar velocity in its tangential components for each star. The Oort parameters determined by a straightforward least-squares adjustment in vector spherical harmonics are A=14.0 +/- 1.4, B=13.1 +/- 1.2, K=1.1 +/- 1.8, and C=2.9 +/- 1.4 km s(exp -1) kpc(exp -1). The physical meaning and the implications of these parameters are discussed in the framework of a general linear model of the velocity field. We find a few statistically significant higher degree harmonic terms that do not correspond to any parameters in the classical linear model. One of them, a third-degree electric harmonic, is tentatively explained as the response to a negative linear gradient of rotation velocity with distance from the Galactic plane, which we estimate at approximately -20 km s(exp -1) kpc(exp -1). A similar vertical gradient of rotation velocity has been detected for more distant stars representing the thick disk (z greater than 1 kpc), but here we surmise its existence in the thin disk at z less than 200 pc. The most unexpected and unexplained term within the Ogorodnikov-Milne model is the first-degree magnetic harmonic, representing a rigid rotation of the stellar field about the axis -Y pointing opposite to the direction of rotation. This harmonic comes out with a statistically robust coefficient of 6.2 +/- 0.9 km s(exp -1) kpc(exp -1) and is also present in the velocity field of more distant stars. The ensuing upward vertical motion of stars in the general direction of the Galactic center and the downward motion in the anticenter direction are opposite to the vector field expected from the stationary Galactic warp model.

Galaxy↗
DOE OSTI2020

Differential credibility assessment for statistical downscaling

Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. Here, the approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact relevant CLIMDEX temperature indices that are assigned credibility based on (1) the reproduction of relevant large-scale predictors by the GCMs (i.e. fraction of regression beta-weights derived from predictors that are well-reproduced) and (2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations, between the two GCM and is generally higher for minimum temperature than maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision makers regarding projection confidence, and/or extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Eastern Indigo Snake (Drymarchon couperi) Shelter Site Use in Peninsular Florida and Implications for Habitat Conservation

Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS) is endemic to the southeastern United States and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial generalized linear models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Cool season tortoise burrow use across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.

Bayesian hierarchical modeling↗
NASA NTRS1983

Spectral reflectance of surface soils: Relationships with some soil properties

Using a published atlas of reflectance curves and physicochemical properties of soils, a statistical analysis was carried out. Reflectance bands which correspond to five of the wavebands used by NASA's Thematic Mapper were examined for relationships to specific soil properties. The properties considered in this study include: Sand Content, Silt Content, Clay Content, Organic Matter Content, Cation Exchange Capacity, Iron Oxide Content and Moisture Content. Regression of these seven properties on the mean values of five TM bands produced results that indicate that the predictability of the properties can be increased by stratifying the data. The data was stratified by parent material, taxonomic order, temperature zone, moisture zone and climate (combined temperature and moisture). The best results were obtained when the sample was examined by climatic classes. The middle Infra-red bands, 5 and 7, as well as the visible bands, 2 and 3, are significant in the model. The near Infra-red band, band 4, is almost as useful and should be included in any studies. General linear modeling procedures examined relationships of the seven properties with certain wavebands in the stratified samples.

Kiesewetter, C. H.↗
DOE OSTI2011

Normal Tissue Complication Probability Modeling of Acute Hematologic Toxicity in Cervical Cancer Patients Treated With Chemoradiotherapy

Purpose: To test the hypothesis that increased pelvic bone marrow (BM) irradiation is associated with increased hematologic toxicity (HT) in cervical cancer patients undergoing chemoradiotherapy and to develop a normal tissue complication probability (NTCP) model for HT. Methods and Materials: We tested associations between hematologic nadirs during chemoradiotherapy and the volume of BM receiving {>=}10 and 20 Gy (V{sub 10} and V{sub 20}) using a previously developed linear regression model. The validation cohort consisted of 44 cervical cancer patients treated with concurrent cisplatin and pelvic radiotherapy. Subsequently, these data were pooled with data from 37 identically treated patients from a previous study, forming a cohort of 81 patients for normal tissue complication probability analysis. Generalized linear modeling was used to test associations between hematologic nadirs and dosimetric parameters, adjusting for body mass index. Receiver operating characteristic curves were used to derive optimal dosimetric planning constraints. Results: In the validation cohort, significant negative correlations were observed between white blood cell count nadir and V{sub 10} (regression coefficient ({beta}) = -0.060, p = 0.009) and V{sub 20} ({beta} = -0.044, p = 0.010). In the combined cohort, the (adjusted) {beta} estimates for log (white blood cell) vs. V{sub 10} and V{sub 20} were as follows: -0.022 (p = 0.025) and -0.021 (p = 0.002), respectively. Patients with V{sub 10} {>=} 95% were more likely to experience Grade {>=}3 leukopenia (68.8% vs. 24.6%, p < 0.001) than were patients with V{sub 20} > 76% (57.7% vs. 21.8%, p = 0.001). Conclusions: These findings support the hypothesis that HT increases with increasing pelvic BM volume irradiated. Efforts to maintain V{sub 10} < 95% and V{sub 20} < 76% may reduce HT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗