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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
NASA NTRS2010

Dst Index in the 2008 GEM Modeling Challenge - Model Performance for Moderate and Strong Magnetic Storms

The GEM 2008 modeling challenge efforts are expanding beyond comparing in-situ measurements in the magnetosphere and ionosphere to include the computation of indices to be compared. The Dst index measures the largest deviations of the horizontal magnetic field at 4 equatorial magnetometers from the quiet-time background field and is commonly used to track the strength of the magnetic disturbance of the magnetosphere during storms. Models can calculate a proxy Dst index in various ways, including using the Dessler-Parker Sckopke relation and the energy of the ring current and Biot-Savart integration of electric currents in the magnetosphere. The GEM modeling challenge investigates 4 space weather events and we compare models available at CCMC against each other and the observed values of Ost. Models used include SWMF/BATSRUS, OpenGGCM, LFM, GUMICS (3D magnetosphere MHD models), Fok-RC, CRCM, RAM-SCB (kinetic drift models of the ring current), WINDMI (magnetosphere-ionosphere electric circuit model), and predictions based on an impulse response function (IRF) model and analytic coupling functions with inputs of solar wind data. In addition to the analysis of model-observation comparisons we look at the way Dst is computed in global magnetosphere models. The default value of Dst computed by the SWMF model is for Bz the Earth's center. In addition to this, we present results obtained at different locations on the Earth's surface. We choose equatorial locations at local noon, dusk (18:00 hours), midnight and dawn (6:00 hours). The different virtual observatory locations reveal the variation around the earth-centered Dst value resulting from the distribution of electric currents in the magnetosphere during different phases of a storm.

Rastaetter, Lutz↗
DOE OSTI2015

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model eta(theta), where theta denotes the uncertain, best input setting. Hence the statistical model is of the form y = eta(theta) + c, where epsilon accounts for measurement, and possibly other, error sources. When nonlinearity is present in eta(center dot), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model eta(center dot). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory.

Higdon, Dave↗
DOE OSTI2014

Dynamic nuclear renography kinetic analysis: Four-compartment model for assessing kidney function

Dynamic nuclear renography method produces TACs of kidneys and bladder. Multiple TACs data can be further analyzed to obtain the overview of urinary system's condition. Tracer kinetic analysis was performed using four-compartment models. The system's model consist of four irreversible compartment with four transport constants (k1, k2, k3 and k4). The mathematical expressions of tracer's distributions is fitted to experimental data (TACs) resulting in model constants. This transport constants represent the urinary system behavior, and later can be used for analyzing system's condition. Different intervals of kinetics parameter are clearly shown by abnormal system with respect to the normal one. Furthermore, the system with delayed uptake has 82% lower uptake parameters (k1 and k2) than normal one. Meanwhile, the system with prolonged clearance time has its kinetics parameters k3 or k4 lower than the others. This model is promising for quantitatively describe urinary system's function especially if supplied with more data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Investigation of Cody–Lorentz and Tauc–Lorentz Models in Characterizing Dielectric Function of (HfO{sub 2}){sub x}(ZrO{sub 2}){sub 1–x} Mixed Thin Film

In the present study, Tauc–Lorentz and Urbach tail assisted Cody–Lorentz models were intuitively derived, explained, and employed to simulate the imaginary dielectric function of the atomic layer deposited (HfO{sub 2}){sub x}(ZrO{sub 2}){sub 1–x} in the energy range of 0.7–9.3 eV. A fitting procedure is carried out using the Levenberg–Marquardt method for minimizing mean square errors. Both models support the experimental data derived by vacuum ultraviolet spectroscopic ellipsometry. For a zirconium content of 60 and 75%, a weak absorption profile is observed in the visible region and elaborately explained by the Lorentz oscillator model. In order to calculate the band gap of different compositions, Cody plots and Tauc plots are presented. The results show that the Cody–Lorentz model is more accuracy due to its exponential absorption below the band gap energy and modified density of states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

The Sensitivity of Chemical Kinetic Models on Flame Transfer Functions in Acoustic Fluctuation Environments with Varying Equivalence Ratios

Various factors affect the heat release response of flames – thermodynamics, hydrodynamics, acoustics, etc. The coupling of heat release rate and acoustics, in particular, is essential to the design of combustion devices so as to avoid adverse effects during their operation. Modeling flames in acoustic environments requires an accurate understanding of the fundamental physics that occurs when flames interact with a fluctuating pressure field. Over the past couple of decades, researchers have been quantifying the impact of this fluctuating pressure field on the unsteady heat release rate. The flame transfer functions computed have also been shown to depend on the level of detail in the chemical kinetic model. This paper attempts to bridge the gap between these studies to understand how sensitive the flame transfer function is to various kinetic models. The problem is studied by performing multiple one-dimensional simulations using the low-Mach solver NGA. The detailed GRI-Mech 3.0 will be used for comparison. Also, the pressure amplitude and reactant equivalence ratio are varied to understand how the environment affects this sensitivity.

Girdhar, Anant↗
DOE OSTI2010

The NJL-jet model for quark fragmentation functions

A description of fragmentation functions which satisfy the momentum and isospin sum rules is presented. We concentrate on the pion fragmentation function, taking into account cascade-like processes in a generalized jet-model approach. Numerical results obtained in this NJL-jet model are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Hydrodechlorination of 1,2-Dichloroethane on Platinum Catalysts: Insights from Reaction Kinetics Experiments, Density Functional Theory, and Microkinetic Modeling

Catalytic hydrodechlorination is a promising strategy for treating industrial 1,2-dichloroethane wastes, for which Pt and Pt-based alloy catalysts are widely used. Here, we performed a detailed mechanistic study for 1,2-dichloroethane hydrodechlorination on Pt using a synergistic approach combining density functional theory (DFT) calculations, reaction kinetics experiments, and microkinetic modeling. Using planewave DFT calculations, we evaluated the reaction energy and activation energy barrier of each elementary step involved in the reaction network on Pt(111). The calculated energetics were then incorporated into a comprehensive mean-field microkinetic model accounting for a total of 65 elementary steps. The model-predicted reaction rates were compared with the results from our reaction kinetics experiments on SiO2-supported Pt catalysts. Our results indicated that the hydrodechlorination of 1,2-dichloroethane on Pt(111) starts with a H-removal step; then, it proceeds through a sequence of alternating dechlorination and dehydrogenation steps until vinylidene (CH 2 C*) is formed; finally, CH 2 C* is hydrogenated to the final product, ethane, sequentially via vinyl (CH 2 CH*), ethylene, and ethyl (CH 3 CH 2 *) intermediates. After model parameter adjustments, we achieved good agreement between our theoretical model and experimental results; the adjustments to the calculated parameters are consistent with the typically anticipated coverage effects. Furthermore, our study offers valuable mechanistic insights, which are useful for improving catalysts for this chemistry.

1,2-dichloroethane↗
DOE OSTI2022

Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials

Herein, this article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete-case approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Application of the metabolic modeling pipeline in KBase to categorize reactions, predict essential genes, and predict pathways in an isolate genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

DOE knowledgebase↗
DOE OSTI2014

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. In this paper, we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. In conclusion, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Reduction of spectroscopic overlap across the Z = 8 shell in neutron-rich nuclei

The recent discovery and spectroscopic measurements of 27 O and 28 O suggests the disappearance of the N = 20 shell structure in these neutron-rich oxygen isotopes. We measured one- and two-proton removal cross sections from 27 F and 29 N, respectively, extracting spectroscopic factors and comparing them to shell model overlap functions coupled with eikonal reaction model calculations. The invariant mass technique was used to reconstruct the two-body ( 24 O + n) and three-body ( 24 O + 2n) decay energies from knockout reactions of 27 F (106.2 MeV/u) and 29 Ne (112.8 MeV/u) beams impinging on a 9 Be target. The one-proton removal from 27 F strongly populated the ground state of 26 O and the extracted cross section of $3.4^{+0.3}_{-1.5}$ mb agrees with eikonal model calculations that are normalized by the shell model spectroscopic factors and account for the systematic reduction factor observed for single nucleon removal reactions within the models used. For the two-proton removal reaction from 29 Ne an upper limit of 0.08 mb was extracted for populating states in 27 O decaying though the ground state of 26 O. The measured upper limit for the population of the ground state of 26 O in the two-proton removal reaction from 29 Ne indicates a significant difference in the underlying nuclear structure of 27 F and 29 Ne.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1993

An architecture for object-oriented intelligent control of power systems in space

A control system for autonomous distribution and control of electrical power during space missions is being developed. This system should free the astronauts from localizing faults and reconfiguring loads if problems with the power distribution and generation components occur. The control system uses an object-oriented simulation model of the power system and first principle knowledge to detect, identify, and isolate faults. Each power system component is represented as a separate object with knowledge of its normal behavior. The reasoning process takes place at three different levels of abstraction: the Physical Component Model (PCM) level, the Electrical Equivalent Model (EEM) level, and the Functional System Model (FSM) level, with the PCM the lowest level of abstraction and the FSM the highest. At the EEM level the power system components are reasoned about as their electrical equivalents, e.g, a resistive load is thought of as a resistor. However, at the PCM level detailed knowledge about the component's specific characteristics is taken into account. The FSM level models the system at the subsystem level, a level appropriate for reconfiguration and scheduling. The control system operates in two modes, a reactive and a proactive mode, simultaneously. In the reactive mode the control system receives measurement data from the power system and compares these values with values determined through simulation to detect the existence of a fault. The nature of the fault is then identified through a model-based reasoning process using mainly the EEM. Compound component models are constructed at the EEM level and used in the fault identification process. In the proactive mode the reasoning takes place at the PCM level. Individual components determine their future health status using a physical model and measured historical data. In case changes in the health status seem imminent the component warns the control system about its impending failure. The fault isolation process uses the FSM level for its reasoning base.

Holmquist, Sven G.↗
NASA NTRS2002

A Simplified Model for an Evolving Protoplanetary Nebula

The dynamical evolution of the protoplanetary nebula is investigated using analytical solutions of the surface density transport equations. Constant and beta viscosity turbulence models are compared with a functional analytical model and the well-known alpha viscosity formulation. The beta viscosity model, heretofore used for steady-state disks, is shown to be a viable tool for separating dynamic and thermodynamic properties of an evolving disk.

Davis, Sanford S.↗
NASA NTRSDate not supplied

Constraints on triple oxygen isotope kinetics

Triple oxygen isotope measurements have been used as tracers for classifying meteorites, as indicators of atmospheric chemistry and as a proxy for primary productivity among other uses. These utilities rely on the mass dependency of most chemical reactions: δ17O is proportional to δ18O by roughly a factor of 0.5. However,in detail it is known that mass-dependency factorsvary. Constraints on mass dependency are well established for equilibrium isotope effects, but similar constraints for kinetic isotope effects are lacking.Here, we provide constraints for isotope effects generated by unidirectional chemical reactions (i.e. kinetic isotope effects, KIEs). A simplified Monte Carlo model is used to estimatethe range of kinetic isotope effects. It is found that the range of KIEs exceeds the range of equilibrium isotope effects such that some mass dependent kinetic isotope effects could be mistaken for being mass-independent. For the16O-17O-18O system,Δ′17Ovariations as large 0.8‰ can be generated from a single reaction step. Two case studies from published literature, on the thermal decomposition of calcite (CaCO3) and brucite (Mg(OH)2), are used to test the extreme limits of the modeled results. Isotope fractionationsgenerated fromdensity functional theory models of the surface reaction steps are shownto match the published data. The fractionation models are strictly mass dependent thus this result suggests thermal decomposition of calcite is also mass dependent. Although oxygen is the focus here, our approach applies to any system with more than two isotopes.

J. A. Hayles↗
DOE OSTI2021

Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

Allen, Benjamin↗