Search NASA⌕ Search

SEARCH · Search NASA

Results for “sampling variability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

Use of Uncertainty in Modeling DFLAW Treatment at Hanford - 17115

Average or mean is understood by most; standard deviation (SD) is understood by few. Certainty is easy to model; uncertainty is not. The flowsheet simulation, described in this paper, placed uncertainties in the G2 model of the Hanford Tank Waste Treatment and Immobilization Plant's (WTP) Direct Feed Low-Activity Waste (DFLAW) process for performance and sensitivity evaluations. G2 is a dynamic material balance model that starts and proceeds with operating logic until all the feed is processed. Dynamic material balance is achieved by tracking the flows, storage, and change of all materials within the plant as time increments. Processing equipment and limitations of the flowsheet are modeled (e.g., equipment types, piping, volumes, flowrates, efficiencies, and physical and chemical environments that impact separations, phase equilibriums, and chemical reactions). Operating logic represents the rules and strategies of a running plant. The G2 model simulated both certainty and uncertainty scenarios for comparison purposes. For the uncertainty scenarios, process decisions were made using presumed values to simulate various operational measurement errors and process variabilities. Presumed means that the known G2 values are adjusted to replicate measurement errors and variabilities. These include: 1) volume measurement, 2) sample analysis, 3) melter decontamination factor (DF), 4) glass former weighing, and 5) glass former composition variations. The errors were randomly generated in accordance with SD values of measurement devices, laboratory methods, and glass former composition ranges. The G2 model runs give insight on the DFLAW process' controllability and the expected range of glass properties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

X-Ray Spectral Analyses of AGNs from the 7Ms Chandra Deep Field-South Survey: The Distribution, Variability, and Evolutions of AGN Obscuration

We present a detailed spectral analysis of the brightest active galactic nuclei (AGNs) identified in the 7Ms Chandra Deep Field-South (CDF-S) survey over a time span of 16 years. Using a model of an intrinsically absorbed power-law plus reflection, with possible soft excess and narrow Fe Kα line, we perform a systematic X-ray spectral analysis, both on the total 7Ms exposure and in four different periods with lengths of 2–21 months. With this approach, we not only present the power-law slopes, column densities N{sub H}, observed fluxes, and absorption-corrected 2–10 keV luminosities L {sub X} for our sample of AGNs, but also identify significant spectral variabilities among them on timescales of years. We find that the N{sub H} variabilities can be ascribed to two different types of mechanisms, either flux-driven or flux-independent. We also find that the correlation between the narrow Fe line EW and N{sub H} can be well explained by the continuum suppression with increasing N{sub H}. Accounting for the sample incompleteness and bias, we measure the intrinsic distribution of N{sub H} for the CDF-S AGN population and present reselected subsamples that are complete with respect to N{sub H}. The N{sub H}-complete subsamples enable us to decouple the dependences of N{sub H} on L {sub X} and on redshift. Combining our data with those from C-COSMOS, we confirm the anticorrelation between the average N{sub H} and L {sub X} of AGN, and find a significant increase of the AGN-obscured fraction with redshift at any luminosity. The obscured fraction can be described as f{sub obscured}≈0.42 (1+z){sup 0.60}.

79 ASTRONOMY AND ASTROPHYSICS↗

Ice-nucleating particles (INPs) concentrations from SAIL-Net

This data set contains ice-nucleating particle (INP) concentration spectra collected during the SAIL-Net sampling period, which complemented the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River watershed near Crested Butte, Colorado. SAIL-Net was a distributed aerosol measurement network designed to investigate aerosol variability across complex mountainous terrain. The data set includes samples from multiple SAIL-Net sites, including AOS, Gothic, Snodgrass, Pumphouse, Irwin, and Top. INP concentrations are reported as a function of freezing temperature, together with confidence limits, sampling times, site location, elevation, sampled air volume, and treatment information. These data provide an analysis-ready record of the INPs across the SAIL-Net network.

activation temperature↗

Establishing reference ranges for circulating biomarkers of drug‐induced liver injury in healthy human volunteers 1

Aims The potential of mechanistic biomarkers to improve prediction of drug‐induced liver injury (DILI) and hepatic regeneration is widely acknowledged. We sought to determine reference intervals for new biomarkers of DILI and regeneration, as well as to characterize their natural variability and impact of diurnal variation. Methods Serum samples from 227 healthy volunteers were recruited as part of a cross‐sectional study; of these, 25 subjects had weekly serial sampling over 3 weeks, while 23 had intensive blood sampling over a 24h period. Alanine aminotransferase (ALT), MicroRNA‐122 (miR‐122), High Mobility Group Box‐1 (HMGB1), total Keratin‐18 (K18), caspase‐cleaved Keratin‐18 (ccK18), Glutamate Dehydrogenase (GLDH) and Macrophage Colony‐Stimulating Factor‐1 (CSF‐1) were assayed. Results Reference intervals were established for each biomarker based on the 97.5% quantile (90% CI) following the assessment of fixed effects in univariate and multivariable models. Intra‐individual variability was found to be non‐significant, and there was no significant impact of diurnal variation. Conclusion Reference intervals for novel DILI biomarkers have been described. An upper limit of a reference range might represent the most appropriate mechanism to utilize these data. These data can now be used to interpret data from exploratory clinical DILI studies and to assist their further qualification as required by regulatory authorities.

Jorgensen, Andrea L. [Department of Health Data Sc↗

A Stratigraphic Approach to Characterize the Deposition and Storage of Organic Matter in Reservoir Sediments.

The relationship between carbon burial and sedimentation in reservoirs is unknown, exposing gaps in our fundamental understanding of the transport, processing, and deposition of sediment and organic matter in fluvial and lacustrine systems and contributing to uncertainty in our understanding of the net impact of dams to the global carbon budget. The 2011-2014 removal of two large dams on the Elwha River, Washington State, the largest dam removal yet completed globally, created extensive cutbank exposures of reservoir sediments, allowing the first characterization of the facies architecture of sediments through direct observation in reservoirs worldwide and providing an unparalleled opportunity to 1) assess the relationship between environmental influences, such as and changes in sediment supply, and their expression in the stratigraphic record, 2) assess the relationship between sedimentation processes and detrital organic carbon deposition and storage, and the importance of coarse-grained organic matter and woody debris to the total carbon budget of a reservoir, and 3) apply the insight gained from these reservoirs to evaluate current global estimates of carbon storage in reservoirs and develop a conceptual model of carbon burial in reservoirs to guide further research, as defined by characteristic stratigraphic “types”. Former Lake Mills, the younger, upstream reservoir, was characterized by a tripartite, subaerial Gilbert-style delta which prograded >1 km into the main reservoir from 1927 to 2011. Sediments were composed of coarse-grained topset beds, steeply dipping foreset beds, and a fine-grained, gently dipping prodelta. While individual event horizons were discernible in fine-grained sediments of former Lake Mills, their number and spacing did not correspond to known drawdown or flood events. Former Lake Aldwell, impounded from 1913 to 2011, was initially defined by the rapid progradation of a Gilbert-style, subaerial delta prior to the upstream completion of Glines Canyon Dam. However, the 1927 closure of Glines Canyon Dam upstream caused the delta to evolve to a fine-grained, mouth-bar type delta indicative of low, finer-grained sediment. This evolution, combined with a previously-unrecognized landslide deposit into the upper delta plain, suggests that understanding the exogenic influences on reservoir sedimentation is critical to interpretation and prediction of the sedimentation within individual systems. Former Lake Mills accumulated ~330 Gg of, with depositional-zone average accumulation rates from 229 to 9262 gCm -2 yr -1 , while Former Lake Aldwell accumulated ~ 91 Gg (263 to 2414 gCm -2 yr -1 ). Carbon storage in both reservoirs was dominated by heterogeneous, coarse organic matter and woody debris in the coarse-grained delta slope and relatively coarse-grained prodelta regions of the reservoirs, with little storage in the gravel-dominated, subaerial delta plains. Carbon accumulation in fine-grained lacustrine and prodelta sediments was relatively homogeneous, but turbidity flows from the Gilbert-style delta slope in former Lake Mills delivered significantly more carbon to the prodelta than the mouth-bar style delta of former Lake Aldwell. C:N ratios support interpretation of most organic matter in both reservoirs as allochthonous. Sampling schemes based only on lacustrine and/or prodelta would underestimate of total carbon accumulation by up to 30% in former Lake Aldwell, but the overestimate by up to 47% in former Lake Mills. Global estimates of carbon sequestration rates in reservoir sediments vary by three orders of magnitude, while individual-reservoir estimates vary by four orders of magnitude and over only 37 reservoirs and a literature review of predictive variables suggests weak or contradictory relationships. A conceptual stratigraphic framework of four unique reservoir types suggests that organic matter deposition is intrinsically tied to sedimentation processes and that patterns of carbon storage vary systematically with the stratigraphy of reservoir sediments. Deltaically-dominated reservoirs (whether Gilbert style or shoalwater) appear to store most carbon in their deltaic and prodelta regions, while thalweg-style reservoirs exhibit a bimodal distribution, with allochthonous carbon preferentially routed along the former river thalweg and autochthonous deposited on the former floodplain. Lacustrine-style reservoirs are dominated by suspended sediment deposition and thus relatively homogeneous, but literature suggests these reservoirs are more variable than typically measured. Current methods of reservoir sampling fail to account for this systematic variation and tend to be biased toward fine sediment, suggesting that global reservoir carbon storage is underestimated.

Stratton, Laurel↗

Reprocessing Models for the Optical Light Curves of Hypervariable Quasars from the Sloan Digital Sky Survey Reverberation Mapping Project

We explore reprocessing models for a sample of 17 hypervariable quasars, taken from the Sloan Digital Sky Survey Reverberation Mapping project, which all show coordinated optical luminosity hypervariability with amplitudes of factors ≳2 between 2014 and 2020. We develop and apply reprocessing models for quasar light curves in simple geometries that are likely to be representative of quasar inner environments. In addition to the commonly investigated thin-disk model, we include the thick-disk and hemisphere geometries. The thick-disk geometry could, for instance, represent a magnetically elevated disk, whereas the hemisphere model can be interpreted as a first-order approximation for any optically thick out-of-plane material caused by outflows/winds, warped/tilted disks, and so on. Of the 17 quasars in our sample, 11 are best-fitted by a hemisphere geometry, five are classified as thick disks, and both models fail for just one object. We highlight the successes and shortcomings of our thermal reprocessing models in case studies of four quasars that are representative of the sample. While reprocessing is unlikely to explain all of the variability that we observe in quasars, we present our classification scheme as a starting point for revealing the likely geometries of reprocessing for quasars in our sample and hypervariable quasars in general.

79 ASTRONOMY AND ASTROPHYSICS↗

Dimuon production in the MicroBooNE detector

This analysis studies the production of the rare signal with two muons (dimuon) in the finalstate at µB using the Booster Neutrino Beam (BNB) as source of neutrino interactions. Themain background source for the dimuon signal are pions. Differentiating muons and pions hasbeen challenging at µB because the tracks left by these particles are almost identical. Thisanalysis faces this problem using a supervised classifier algorithm called boosted decisiontree (BDT). A Monte Carlo simulation (MC) enhanced dimuon signal sample was producedto train a BDT capable of recognizing this signal. A set of variables that show somediscrimination characteristics was used to perform the training. A MC neutrino interactionsimulation sample was used to estimate the background. Estimations of dimuon productionin real data samples were made for different cases. Additionally, an analysis of the systematicuncertainties, fake data studies, and BDT performance checks were obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Chemistry↗

A catalogue of cataclysmic variables from 20 yr of the Sloan Digital Sky Survey with new classifications, periods, trends, and oddities

ABSTRACT We present a catalogue of 507 cataclysmic variables (CVs) observed in SDSS I to IV including 70 new classifications collated from multiple archival data sets. This represents the largest sample of CVs with high-quality and homogeneous optical spectroscopy. We have used this sample to derive unbiased space densities and period distributions for the major sub-types of CVs. We also report on some peculiar CVs, period bouncers and also CVs exhibiting large changes in accretion rates. We report 70 new CVs, 59 new periods, 178 unpublished spectra, and 262 new or updated classifications. From the SDSS spectroscopy, we also identified 18 systems incorrectly identified as CVs in the literature. We discuss the observed properties of 13 peculiar CVS, and we identify a small set of eight CVs that defy the standard classification scheme. We use this sample to investigate the distribution of different CV sub-types, and we estimate their individual space densities, as well as that of the entire CV population. The SDSS I to IV sample includes 14 period bounce CVs or candidates. We discuss the variability of CVs across the Hertzsprung–Russell diagram, highlighting selection biases of variability-based CV detection. Finally, we searched for, and found eight tertiary companions to the SDSS CVs. We anticipate that this catalogue and the extensive material included in the Supplementary Data will be useful for a range of observational population studies of CVs.

79 ASTRONOMY AND ASTROPHYSICS↗

Low cadmium exposure in males and lactating females–estimation of biomarkers

Background: Urine cadmium (Cd) and renal function biomarkers, mostly analysed in urine spot samples, are well established biomarkers of occupational exposure. Their use and associations at low environmental level are common, but have recently been questioned, particularly in terms of physiological variability and normalisation bias in the case of urine spot samples. Aim: To determine the appropriateness of spot urine and/or blood Cd exposure biomarkers and their relationships with renal function biomarkers at low levels of exposure. To this end, we used data from Slovenian human biomonitoring program involving 1081 Slovenians (548 males, mean age 31 years; 533 lactating females, mean age 29 years; 2007–2015) who have not been exposed to Cd occupationally. Results: Geometric means (GMs) of Cd in blood and spot urine samples were 0.27 ng/mL (0.28 for males and 0.33 for females) and 0.19 ng/mL (0.21 for males and 0.17 for females), respectively. Differing results were obtained when contrasting normalisation by urine creatinine with specific gravity. GMs of urine albumin (Alb), alpha-1-microglobulin (A1M), N-acetyl-beta-glucosaminidase (NAG), and immunoglobulin G (IgG) were far below their upper reference limits. Statistical analysis of unnormalised or normalised urine data often yielded inconsistent and conflicting results (or trends), so association analyses with unnormalised data were taken as more valid. Relatively weak positive associations were observed between urine Cd (ng/mL) and blood Cd (β=0.11, p=0.002 for males and β=0.33, p<0.001 for females) and for females between urine NAG and blood Cd (β=0.14, p=0.04). No associations were found between other renal function biomarkers and blood Cd. Associations between Cd and renal function biomarkers in urine were stronger (p<0.05, β=0.11–0.63). Mostly, all of the associations stayed significant but weakened after normalisation for diuresis. In the case of A1M, its associations with Cd were influenced by current smoking and blood Pb in males and by pre-pregnancy smoking and blood Se in females (β up to 0.34, p<0.001). Statistical analysis of unnormalised or normalised urine data often yielded inconsistent and conflicting results (or trends), so association analyses data with unnormalised were taken as more valid. Conclusions: The observed uncertainties introduced by urine normalisation, particularly by creatinine, confirm blood Cd as a superior low-Cd exposure biomarker versus urine Cd in cases when 24 h urine is unattainable. Evidence that A1M can be positively related to Cd, smoking (current or pre-pregnancy), Pb, and Se status, points to the versatile biological functions of A1M. - Highlights: • Creatinine normalisation of spot urine data is inappropriate at low Cd exposure. • Blood Cd as low-Cd exposure biomarker is superior over spot urine Cd. • Cd in urine, but not in blood, was significantly associated with A1M. • A1M – Cd relations were influenced by smoking, Se and less so by Pb.

60 APPLIED LIFE SCIENCES↗

Electrical Properties of Intercalated Ag{sub x}MoSe{sub 2} Compounds in Constant and Alternating Fields

On intercalated Ag{sub x}MoSe{sub 2} samples, in addition to temperature measurements of the direct current electrical resistivity, measurements of the alternating current resistivity using the impedance spectroscopy technique are carried out in a wide frequency range and at different temperatures. The activation behavior of the d.c. conductivity, which increases with increasing silver content in the samples, is shown. The a.c. conductivity undergoes frequency dispersion, described by a “universal dynamic response” (UDR). It is shown that the relaxation processes during charge transfer in a variable field are accelerated with increasing silver content in the samples and with increasing temperature. The data obtained are analyzed using the models of the band and hopping conduction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗

Effects of finite sampling on fatigue damage estimation of wind turbine components: A statistical study

The variability of the wind turbine loads complicates fatigue assessment in the design phase, as performing simulations covering the entire lifetime is computationally expensive. The current work provides important information for assessing the uncertainty in fatigue damage estimation due to finite data. Here we study the sample size effect on mean, variance, and skewness of damage in each wind bin, identify the important wind bins, and study the uncertainty propagation from each wind bin to the lifetime damage using 3600 aeroelastic simulations and bootstrapping. To achieve less than 1% error in the damage estimation across all load channels in the current case study, at least 100 turbulence seeds are needed. Damage in different wind bins follows a lognormal distribution when using the conventional approach of six seeds. The provided insights and information allow the designer to achieve a specific level of accuracy for a given computational cost using strategic bin sampling.

17 WIND ENERGY↗

Low-Activity Waste Glass Standards Preparation and Characterization for Calibration of Analytical Instruments

A matrix of seven low-activity waste (LAW) glasses was fabricated and characterized to be used by the Hanford Waste Treatment and Immobilization Plant (WTP) as analytical instrument calibration and matrix interference standards when measuring LAW glass compositions. The matrix of glasses was designed to represent the range of LAW glass compositions that will be generated by the WTP. Samples from each of the seven glasses were measured with electron probe microanalysis (EPMA), ion chromatography (IC), and bulk inductively coupled plasma mass spectroscopy (ICP-MS) to determine the variability and accuracy across batches and within individually poured glass bar samples. High relative percent differences and standard deviations were calculated for components with low concentrations due to instrument detection limits. IC and ICP-MS percent differences and standard deviations were higher at low component concentrations. Otherwise, variability in measured compositions from IC and ICP-MS was generally lower than measurements from EPMA, with the exclusion of Cl, F, Si, and B. Overall, the two methods proved comparable and complementary. From EPMA, IC, and ICP-MS data, volatile elements were underrepresented compared to batched compositions, which suggested loss of these elements during melting. EPMA data was statistically analyzed to evaluate homogeneity within single bars and whole compositions for each of the seven glasses. Most of the variability within glass compositions occurred at the bar-to-bar level; however, no clear systematic trends were observed. Based on these analyses, when using these glasses as reference material, it is recommended that the overall mean and variance of each composition be used for performing instrument calibrations and analytical corrections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Time variability of ultra fast BAL outflows using SALT: C IV equivalent width analysis

We study the time variability (over ≤7.3 yr) of ultra fast outflows (UFOs) detected in a sample of 64 C IV broad absorption line (BAL) quasars (with 80 distinct BAL components) monitored using the Southern African Large Telescope. By comparing the properties of the quasar in our sample with those of a control sample of non-BAL quasars, we show that the distributions of black hole mass are different and the bolometric luminosities and optical photometric variations of UFO BAL quasars are slightly smaller compared to that of non-BAL quasars. The detection fraction of C IV equivalent width (W) variability (∼95 per cent), the fractional variability amplitude $\left(\frac{\Delta W}{W}\right)$ and the fraction of ‘highly variable’ BAL (i.e. $\big|$$\frac{\Delta W}{W}$$\big|$ > 0.67) components (∼33 per cent) are higher in our sample compared to the general BAL population. The scatter in $\frac{\Delta W}{W}$ and the fraction of ‘highly variable’ BALs increase with the time-scale probed. The $\frac{\Delta W}{W}$ distribution is asymmetric at large time scales. We attribute this to the BAL strengthening time-scales being shorter than the weakening time-scales. The BAL variability amplitude correlates strongly with the BAL properties compared to the quasar properties. BALs with low W, high-velocity, shallow profiles, and low-velocity width tend to show more variability. When multiple BAL components are present, a correlated variability is seen between low- and high-velocity components with the latter showing a larger amplitude variations. We find an anticorrelation between the fractional variations in the continuum flux and W. While this suggests photoionization induced variability, the scatter in continuum flux is much smaller than that of W.

79 ASTRONOMY AND ASTROPHYSICS↗

Integrated Component-Specific Measurements to Develop Emission Factors for Compressors and Gas Gathering Lines

The Department of Energy (DOE) National Energy Technology Laboratory (NETL) and other critical stakeholders (e.g., U.S. Environmental Protection Agency (USEPA), state regulators, and industry) seek to more accurately characterize and quantify methane emissions from compressors and gathering lines within the Gathering and Boosting (G&B) segment of the natural gas system network. These efforts will reduce uncertainties in emissions estimates in USEPA’s current Green House Gas Inventory (GHGI) under their Greenhouse Gas Reporting Program (GHGRP). For this study, four G&B stations in the Gulf Coast Area were visited four times over the course of one year. Components were classified according to equipment and component type and emissions from select components were measured using multiple measurement technologies. The following major equipment types were considered: i) compressors, ii) separators, iii) dehydrators, iv) coalescers, v) slug catchers, vi) yard piping, vii) tanks, and viii) gathering lines. Individual components on each equipment type were further divided into the following main categories: i) connectors, ii) valves, iii) pressure relief valves (PRV), iv) meters, v) gauges, vi) regulators, vii) pneumatic device vents, and viii) compressor vents (e.g., distance piece vents [DPVs] and rod packing vents [RPVs]). Components were screened for the presence of leaks and venting emissions with an optical gas imaging (OGI) camera, and methane emission rates were measured using a microdilution high volume sampler. Downwind emissions were measured using an Open Path Fourier Transform Infrared (OP-FTIR) Spectrometer. Data were evaluated to assess correlation with site, equipment, time, and operational variables. Key findings and recommendations from the study include: A) Different counting methodologies can result in vastly different component counts, thus influencing total emissions calculations using Subpart W population EFs. A standard methodology should be adopted for classifying and counting components in order to minimize inconsistencies across studies and instruct stakeholders responsible for GHGI reporting. B) Results support current component subcategories listed in USEPA Subpart W, including the subdivision of connectors (flanged and “other”) and pneumatic device vents (intermittent, continuous low bleed, and continuous high bleed). C) Emission rates vary across sites and a relationship between higher emissions and compressor age was observed for some components. D) Component emission rates varied between field campaigns. The type of components contributing to overall measured emissions also varied among field campaigns. However, the emission rate variability was larger between different components (of the same category) than the variability in one component over the four field campaigns. Therefore, when calculating emission factors, it is not necessary to take repeat measurements to capture component variability. E) Measuring the emission rate of a sample for an extended period of time is not necessary to capture the average component emission rate. F) For reporting purposes, excluding liquid lines from site-wide screening may have a negligible effect on component-related emissions. However, from a site management perspective, malfunctioning components were not uncommon on liquid lines and contributed substantially to fugitive emissions at some sites. G) Population EFs, whether including or excluding liquid lines, were a conservative estimate of fugitive emissions. Leaker EFs consistently underestimated emissions at the field sites. Measurements at more sites, particularity sites in other areas of the country, need to be completed before determining the accuracy of EFs.

03 NATURAL GAS↗

Polynomial chaos expansions for dependent random variables

Polynomial chaos expansions (PCE) are well-suited to quantifying uncertainty in models parameterized by independent random variables. The assumption of independence leads to simple strategies for building multivariate orthonormal bases and for sampling strategies to evaluate PCE coefficients. In contrast, the application of PCE to models of dependent variables is much more challenging. Three approaches can be used to construct PCE of models of dependent variables. The first approach uses mapping methods where measure transformations, such as the Nataf and Rosenblatt transformation, can be used to map dependent random variables to independent ones; however we show that this can significantly degrade performance since the Jacobian of the map must be approximated. A second strategy is the class of dominating support methods. In these approaches a PCE is built using independent random variables whose distributional support dominates the support of the true dependent joint density; we provide evidence that this approach appears to produce approximations with suboptimal accuracy. A third approach, the novel method proposed here, uses Gram-Schmidt orthogonalization (GSO) to numerically compute orthonormal polynomials for the dependent random variables. This approach has been used successfully when solving differential equations using the intrusive stochastic Galerkin method, and in this paper we use GSO to build PCE using a non-intrusive stochastic collocation method. The stochastic collocation method treats the model as a black box and builds approximations of the input-output map from a set of samples. Building PCE from samples can introduce ill-conditioning which does not plague stochastic Galerkin methods. To mitigate this ill-conditioning we generate weighted Leja sequences, which are nested sample sets, to build accurate polynomial interpolants. We show that our proposed approach, GSO with weighted Leja sequences, produces PCE which are orders of magnitude more accurate than PCE constructed using mapping or dominating support methods.

97 MATHEMATICS AND COMPUTING↗