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At least 199 records · Page 11

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

An industrial fluidized bed reactor was designed to convert an aqueous solid laden stream into a consistent granular product. CFD simulations were run using the MFiX two-fluid model for this fluidizing bed operating at 650 °C. A set of simulations were run over a Latin-hypercube sample of five model parameters – bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate. Data from the simulations were collected on three quantities of interest – bed differential temperature, low solids velocity, and bed voidage. The data presented here is the full span of response surfaces generated using the process Gaussian response surface model in the Dakota code, as well as the table of data for coefficients of the fitted model. The fits to the five-dimensional Gaussian Process models were 0.7797, 0.8664, and 0.9440 for the temperature, velocity, and solids packing, respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

ASP (Adaptive Splines for Prediction) [SWR-22-66]

This software package provides functionality to fit piecewise cubic splines to model the relationship between a univariate X (independent variable) and a univariate Y (dependent variable). The output is akin to linear regression but has higher "capacity" in that it can learn more complicated relationships despite leveraging only a single predictor. It thus can learn very reasonable relationships between X and Y with minimal effort. The canonical intended use case is for predicting electric demand (Y) from temperature (X). There can be complicated, non-linear relationships between these two variables, but these can be reasonably approximated by the sum of: 1) an "aggregate" relationship between daily average temperature and daily average demand, and 2) intra-day patterns represented as hourly offsets from the daily average load. The spline functionality is used for learning a robust "aggregate" relationship without the inconvenience of manual feature engineering while still ensuring robust results. The model fitting workhorse is R's built-in smooth.spline. This package provides a suite of options for controlling how smooth.spline fits a spline to the data. In particular, it is designed to return a model with a single critical point (i.e., a single location along the domain/support where the first derivative is zero). This is to ensure that predicted changes in electric demand are always positive as temperatures become more extreme. This helps to prevent overfitting, particularly when the input data has few observations or has significant uncertainty from other sources.

Murphy, Sinnott↗

DOE OSTI2015

Near-IR polarized scattered light imagery of the DoAr 28 transitional disk

We present the first spatially resolved polarized scattered light H-band detection of the DoAr 28 transitional disk. Our two epochs of imagery detect the scattered light disk from our effective inner working angle of 0.″10 (13 AU) out to 0.″50 (65 AU). This inner working angle is interior to the location of the system’s gap inferred by previous studies using spectral energy distribution modeling (15 AU). We detected a candidate point source companion 1.″08 northwest of the system; however, our second epoch of imagery strongly suggests that this object is a background star. We constructed a grid of Monte Carlo Radiative Transfer models of the system, and our best fit models utilize a modestly inclined (50°), 0.01 M{sub ⊙} disk that has a partially depleted inner gap from the dust sublimation radius out to ∼8 AU. Subtracting this best fit, axi-symmetric model from our polarized intensity data reveals evidence for two small asymmetries in the disk, which could be attributable to a variety of mechanisms.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2015

Near-IR Polarized Scattered Light Imagery of the DoAr 28 Transitional Disk

We present the first spatially resolved polarized scattered light H-band detection of the DoAr 28 transitional disk. Our two epochs of imagery detect the scattered light disk from our effective inner working angle of 0 double prime.10 (13 AU) out to 0double prime.50 (65 AU). This inner working angle is interior to the location of the system's gap inferred by previous studies using spectral energy distribution modeling (15 AU). We detected a candidate point source companion 1 double prime.08 northwest of the system; however, our second epoch of imagery strongly suggests that this object is a background star. We constructed a grid of Monte Carlo Radiative Transfer models of the system, and our best fit models utilize a modestly inclined (50 degrees), 0.01 solar mass disk that has a partially depleted inner gap from the dust sublimation radius out to approximately 8 AU. Subtracting this best fit, axi-symmetric model from our polarized intensity data reveals evidence for two small asymmetries in the disk, which could be attributable to a variety of mechanisms.

polarized scattered light H-band↗

DOE OSTI2019

Elucidation of Fe-N-C electrocatalyst active site functionality via in-situ X-ray absorption and operando determination of oxygen reduction reaction kinetics in a PEFC

In the past decade the notable effort placed on improving intrinsic electrochemical kinetics of platinum group metal (PGM)-free electrocatalysts for the oxygen reduction reaction (ORR) has led to a significant improvement in both performance and understanding of this class of electrocatalysts. However, a limited amount of this development and understanding has been undertaken using operando electrochemical diagnostics at the membrane electrode assembly (MEA) level. In this work, the operando ORR kinetics on an atomically dispersed iron-nitrogen-carbon ((AD)Fe-N-C) PGM-free electrocatalyst have been examined to extract the reaction order and the activation energy of the ORR. The experiments were carefully designed to ensure the stability/predictability of the electrocatalyst during the data collection process and thus validate the relevance of the values obtained for the aforementioned parameters. A kinetic model that considers a potential-dependent availability of active sites (θ) is proposed. Active site availability is shown to be a function of both the change in the oxidation state (nR) and the redox potential at which the metal center transitions from a higher oxidation state to a lower one (). The resulting model fitting parameters for nR and (0.71 and 0.788 V, respectively) obtained from the analysis of operando data correlate well with those from in situ X-ray absorption near edge structure measurements (nR=0.57) and in situ cyclic voltammetry measurements (0.75 V < <0.8 V) in the MEA environment. The resulting model provides an excellent fit of MEA performance across the range of pressures, temperatures, and potentials under which the data were collected.

Activation energy↗

NASA NTRS1994

Preliminary estimates of Gulf Stream characteristics from TOPEX data and a precise gravimetric geoid

TOPEX sea surface height data has been used, with a gravimetric geoid, to calculate sea surface topography across the Gulf Stream. This topography was initially computed for nine tracks on cycles 21 to 29. Due to inaccurate geoid undulations on one track, results for eight tracks are reported. The sea surface topography estimates were used to calculate parameters that describe Gulf Stream characteristics from two models of the Gulf Stream. One model was based on a Gaussian representation of the velocity while the other was a hyperbolic representation of velocity or the sea surface topography. The parameters of the Gaussian velocity model fit were a width parameter, a maximum velocity value, and the location of the maximum velocity. The parameters of the hyperbolic sea surface topography model were the width, the height jump, position, and sea surface topography at the center of the stream. Both models were used for the eight tracks and nine cycles studied. Comparisons were made between the width parameters, the maximum velocities, and the height jumps. Some of the parameter estimates were found to be highly (0.9) correlated when the hyperbolic sea surface topography fit was carried out, but such correlations were reduced for either the Gaussian velocity fits or the hyperbolic velocity model fit. A comparison of the parameters derived from 1-year TOPEX data showed good agreement with values derived by Kelly (1991) using 2.5 years of Geosat data near 38 deg N, 66 deg W longitude. Accuracy of the geoid undulations used in the calculations was of order of +/- 16 cm with the accuracy of a geoid undulation difference equal to +/- 15 cm over a 100-km line in areas with good terrestrial data coverage. This paper demonstrates that our knowledge or geoid undulations and undulation differences, in a portion of the Gulf Stream region, is sufficiently accurate to determine characteristics of the jet when used with TOPEX altimeter data. The method used here has not been shown to be more accurate than methods that average altimeter data to form a reference surface used in analysis to obtain the Gulf Stream characteristics. However, the results show the geoid approach may be used in areas where lack of current meandering reduces the accuracy of the average surface procedure.

Rapp, Richard H.↗

DOE OSTI2014

SU-C-9A-04: Alternative Analytic Solution to the Paralyzable Detector Model to Calculate Deadtime and Deadtime Loss

Purpose: Some common methods to solve for deadtime are (1) dual-source method, which assumes two equal activities; (2) model fitting, which requires multiple acquisitions as source decays; and (3) lossless model, which assumes no deadtime loss at low count rates. We propose a new analytic alternative solution to calculate deadtime for paralyzable gamma camera. Methods: Deadtime T can be calculated analytically from two distinct observed count rates M1 and M2 when the ratio of the true count rates alpha=N2/N1 is known. Alpha can be measured as a ratio of two measured activities using dose calibrators or via radioactive decay. Knowledge of alpha creates a system with 2 equations and 2 unknowns, i.e., T and N1. To verify the validity of the proposed method, projections of a non-uniform phantom (4GBq 99mTc) were acquired in using Siemens SymbiaS multiple times over 48 hours. Each projection has >100kcts. The deadtime for each projection was calculated by fitting the data to a paralyzable model and also by using the proposed 2-acquisition method. The two estimates of deadtime were compared using the Bland-Altmann method. In addition, the dependency of uncertainty in T on uncertainty in alpha was investigated for several imaging conditions. Results: The results strongly suggest that the 2-acquisition method is equivalent to the fitting method. The Bland-Altman analysis yielded mean difference in deadtime estimate of ∼0.076us (95%CI: -0.049us, 0.103us) between the 2-acquisition and model fitting methods. The 95% limits of agreement were calculated to be -0.104 to 0.256us. The uncertainty in deadtime calculated using the proposed method is highly dependent on the uncertainty in the ratio alpha. Conclusion: The 2-acquisition method was found to be equivalent to the parameter fitting method. The proposed method offers a simpler and more practical way to analytically solve for a paralyzable detector deadtime, especially during physics testing.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Optimization and supervised machine learning methods for fitting numerical physics models without derivatives

Here, we address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of particular interest is the performance of optimization-based training algorithms when dozens, rather than millions or more, of training data are available and when the expense of the model places limitations on the number of concurrent model evaluations that can be performed. As a case study, we consider the Fayans energy density functional model, which has characteristics similar to many model fitting and calibration problems in nuclear physics. We analyze hyperparameter tuning considerations and variability associated with stochastic optimization algorithms and illustrate considerations for tuning in different computational settings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Survival of Immature Gopher Tortoises Recruited into a Translocated Population

Population manipulations such as translocation and head-starting are increasingly used as recovery tools for chelonians. However, evaluating success of individual projects can require decades of monitoring to detect population trends in these long-lived species. Furthermore, there are often few benchmarks from stable, unmanipulated populations against which to compare demographic rates, particularly for the immature stages. Here, we used 8 years of mark-recapture data to estimate apparent survival of immature gopher tortoises recruited into an introduced population of gopher tortoises (Gopherus polyphemus) first established on St. Catherines Island, Georgia, USA in 1987. During 2006 29 -2013, we conducted targeted trapping of immature gopher tortoises and compared survival of the hatchling, juvenile and subadult stages among treatments: 1) individuals released shortly after hatching from eggs obtained from gravid female founders (‘direct releases’); 2) individuals reared in captivity for 6-9 months following hatching (‘head-starts’); and 3) individuals first encountered as free-ranging, wild-recruited offspring (‘wild recruits’). Among the candidate models we examined, the best fit model included additive effects of tortoise stage and treatment, however, overlapping 95% credible intervals among treatments (CI) suggested that survival did not vary significantly among treatments. Annual apparent survival increased over the immature period, highlighting the importance of calculating separate estimates for the different immature stages. Across all treatments, the additive model estimated annual apparent survival probability to be 0.37 (CI: 0.25 40 – 0.48) for hatchlings, 0.71 (CI: 0.61 – 0.81) for juveniles, and 0.83 (CI: 0.74 – 0.94) for subadults. Our study, in combination with previous monitoring efforts at St. Catherines Island, provides strong evidence that the translocation and subsequent population augmentation efforts have been successful in establishing a robust population of gopher tortoises. Additionally, our results provide estimates of demographic rates for life stages that are poorly understood but critical to understanding population dynamics of this imperiled species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Differentiating Structural Changes of Glycoproteins in Solution using Small Angle Scattering Analysis

Alpha 1-Antitrypsin (A1AT) and Fibrinogen are plasma glycoproteins with different, but specific biological functions. A1AT (which forms part of the serpin family) has been shown to have protective roles of lung cells against emphysema, a disease characterized by lung tissue destruction, while fibrinogen is a major factor in the blood clotting process. While biologically distinct, the two proteins present similarities in biophysical studies due to their size and polymerization tendency. Previous approximations, using radius of gyration analysis (shape independent), have shown that there is a phase transition for A1AT under osmotic stress. The goals of this study are to better understand: the stability of A1AT's native conformation when its environment deviates from physiological conditions; and the effects on fibrin's final structure as fibrinogen undergoes polymerization under different conditions. Environmental variables include osmotic stress, crowding, and presence of other molecules (e.g. lipids, catalytic enzyme thrombin, or cholesterol) found in plasma. The method used to assess the stability of the native conformation was to observe changes in the structural envelope of the protein. Static and dynamic small angle x-ray scattering (SAXS) and small angle neutron scattering (SANS) experiments of the proteins in solution were conducted for this purpose. Scattering data was fitted using various shape dependent models in SASView. Pair distance functions (PDF) were used to validate any changes identified by the model fits. Multi-variable phase diagrams were constructed from the fitting results in order to quantify structural and conformational changes of the proteins.

Segally, Taylor N.↗

DOE OSTI2016

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts to a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. Here, we develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1977

Driver steering dynamics measured in car simulator under a range of visibility and roadmaking conditions

A simulation experiment was conducted to determine the effect of reduced visibility on driver lateral (steering) control. The simulator included a real car cab and a single lane road image projected on a screen six feet in front of the driver. Simulated equations of motion controlled apparent car lane position in response to driver steering actions, wind gusts, and road curvature. Six drivers experienced a range of visibility conditions at various speeds with assorted roadmaking configurations (mark and gap lengths). Driver describing functions were measured and detailed parametric model fits were determined. A pursuit model employing a road curvature feedforward was very effective in explaining driver behavior in following randomly curving roads. Sampled-data concepts were also effective in explaining the combined effects of reduced visibility and intermittent road markings on the driver's dynamic time delay. The results indicate the relative importance of various perceptual variables as the visual input to the driver's steering control process is changed.

Allen, R. W.↗

NASA NTRS2015

NASA Galactic Cosmic Radiation Environment Model: Badhwar-O'Neill (2014)

The Badhwar‐O'Neill (BON) Galactic Cosmic Ray (GCR) flux model is used by NASA to certify microelectronic systems and in the analysis of radiation health risks for human space flight missions. Of special interest to NASA is the kinetic energy region below 4.0 GeV/n due to the fact that exposure from GCR behind shielding (e.g., inside a space vehicle) is heavily influenced by the GCR particles from this energy domain. The BON model numerically solves the Fokker‐Planck differential equation to account for particle transport in the heliosphere due to diffusion, convection, and adiabatic deceleration under the assumption of a spherically symmetric heliosphere. The model utilizes a GCR measurements database from various particle detectors to determine the boundary conditions. By using an updated GCR database and improved model fit parameters, the new BON model (BON14) is significantly improved over the previous BON models for describing the GCR radiation environment of interest to human space flight.

O'Neill, P. M.↗

NASA NTRS2015

NASA Galactic Cosmic Radiation Environment Model: Badhwar - O'Neill (2014)

The Badhwar-O'Neill (BON) Galactic Cosmic Ray (GCR) flux model has been used by NASA to certify microelectronic systems and in the analysis of radiation health risks for human space flight missions. Of special interest to NASA is the kinetic energy region below 4.0 GeV/n due to the fact that exposure from GCR behind shielding (e.g., inside a space vehicle) is heavily influenced by the GCR particles from this energy domain. The BON model numerically solves the Fokker-Planck differential equation to account for particle transport in the heliosphere due to diffusion, convection, and adiabatic deceleration under the assumption of a spherically symmetric heliosphere. The model utilizes a comprehensive database of GCR measurements from various particle detectors to determine boundary conditions. By using an updated GCR database and improved model fit parameters, the new BON model (BON14) is significantly improved over the previous BON models for describing the GCR radiation environment of interest to human space flight.

Golge, S.↗

DOE OSTI2022

A Search for Sterile-Neutrino-Based Muon Neutrino Disappearance Using the MicroBooNe Deep Learning Analysis

We describe a search for νµ disappearance using the MicroBooNE Deep Learning analysis 1µ1p selection. Presently, the unexplained MiniBooNE and LSND anomalies could be explained by a sterile neutrino impacting neutrino oscillations. Our analysis searches for the allowed parameter space that could describe such a sterile neutrino. We determine the allowed and excluded region of a 3+1 sterile-based muon neutrino disappearance model in MicroBooNE at 90% confidence. Our allowed region includes both the null model, and current global best fit model. Context for the underlying Deep Learning analysis is provided and several validation studies surrounding both the disappearance search, and originating 1µ1p selection are performed to strengthen confidence in the result. In addition, a next-generation deep learning tool for cosmic-ray-muon event discrimination is proposed and evaluated, demonstrating a removal of 70% of the remaining event background when added to current methods, under the cut criteria used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

A Search for Sterile-Neutrino-Based Muon Neutrino Disappearance Using the MicroBooNe Deep Learning Analysis

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

The near-nucleus gas coma of comet 67P/Churyumov-Gerasimenko prior to the descent of the surface lander PHILAE

Context. The European Space Agency (ESA) Rosetta mission was the most comprehensive study of a comet ever performed. In particular, the Rosetta orbiter, which carried many instruments for monitoring the evolution of the dusty gas emitted by the cometary nucleus, returned an enormous volume of observational data collected from the close vicinity of the nucleus of comet 67P/Churyumov-Gerasimenko. Aims. Such data are expected to yield unique information on the physical processes of gas and dust emission, using current physical model fits to the data. We present such a model (the RZC model) and our procedure of adjustment of this model to the data. Methods. The RZC model consists of two components: (1) a numerical three-dimensional time-dependent code solving the Eulerian/Navier-Stokes equations governing the gas outflow, and a direct simulation Monte Carlo (DSMC) gaskinetic code with the same objective; and (2) an iterative procedure to adjust the assumed model parameters to best-fit the observational data at all times. Results. We demonstrate that our model is able to reproduce the overall features of the local neutral number density and composition measurements of Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) Comet Pressure Sensor (COPS) and Double Focusing Mass Spectrometer (DFMS) instruments in the period August 1–November 30, 2014. The results of numerical simulations show that illumination conditions on the nucleus are the main driver for the gas activity of the comet. We present the distribution of surface inhomogeneity best-fitted to the ROSINA COPS and DFMS in situ measurements.

79 ASTRONOMY AND ASTROPHYSICS↗