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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Radiation Anomaly Detection and Classification with Bayes Model Selection

We present a new method for radiation anomaly detection that is based on Bayes Model Selection (BMS), together with models for gamma-radiation measurements from benign and threat sources. The method estimates the relative odds of pairs of such models, with the aim of supporting related hypotheses about the nature of the underlying source material. We also discuss partial optimization of the parameters in the models. The method allows measurements to be broadly categorized and screened for sources of interest in real time, a property that should improve the efficiency of mobile search or unattended monitoring operations.

Anomaly detection, Gamma-ray spectroscopy, Radiati↗
DOE OSTI2016

Development of a PBPK model of thiocyanate in rats with an extrapolation to humans: A computational study to quantify the mechanism of action of thiocyanate kinetics in thyroid

Thyroid homeostasis can be disturbed due to thiocyanate exposure from the diet or tobacco smoke. Thiocyanate inhibits both thyroidal uptake of iodide, via the sodium-iodide symporter (NIS), and thyroid hormone (TH) synthesis in the thyroid, via thyroid peroxidase (TPO), but the mode of action of thiocyanate is poorly quantified in the literature. The characterization of the link between intra-thyroidal thiocyanate concentrations and dose of exposure is crucial for assessing the risk of thyroid perturbations due to thiocyanate exposure. We developed a PBPK model for thiocyanate that describes its kinetics in the whole-body up to daily doses of 0.15 mmol/kg, with a mechanistic description of the thyroidal kinetics including NIS, passive diffusion, and TPO. The model was calibrated in a Bayesian framework using published studies in rats. Goodness-of-fit was satisfactory, especially for intra-thyroidal thiocyanate concentrations. Thiocyanate kinetic processes were quantified in vivo, including the metabolic clearance by TPO. The passive diffusion rate was found to be greater than NIS-mediated uptake rate. The model captured the dose-dependent kinetics of thiocyanate after acute and chronic exposures. Model behavior was evaluated using a Morris screening test. The distribution of thiocyanate into the thyroid was found to be determined primarily by the partition coefficient, followed by NIS and passive diffusion; the impact of the latter two mechanisms appears to increase at very low doses. Extrapolation to humans resulted in good predictions of thiocyanate kinetics during chronic exposure. The developed PBPK model can be used in risk assessment to quantify dose-response effects of thiocyanate on TH. - Highlights: • A PBPK model of thiocyanate (SCN{sup −}) was calibrated in rats in a Bayesian framework. • The intra-thyroidal kinetics of thiocyanate including NIS and TPO was modeled. • Passive diffusion rate for SCN{sup −} seemed to be greater than the NIS-mediated uptake. • The dose-dependent kinetics of SCN{sup −} was captured after an acute and chronic exposure. • The PBPK model of thiocyanate was successfully extrapolated to humans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Challenges in Modeling Electrochemical Reaction Energetics with Polarizable Continuum Models

A major challenge in the modeling of electrochemical phenomena is the accurate description of the interface between an electrolyte and a charged conductor. Polarizable continuum models (PCM) have been gaining popularity because they offer a computationally inexpensive method of modeling the electrolyte. In this Perspective, we discuss challenges from using one such model which treats the ions using a linearized Poisson–Boltzmann (LPB) distribution. From a physical perspective, this model places charge unphysically close to the surface and adsorbates, and it includes excessively steep ramping of the dielectric constant from the surface to the bulk solvent. Both of these issues can be somewhat mitigated by adjusting parameters built into the model, but in doing so, the resultant capacitance deviates from experimental values. Likewise, hybrid explicit-implicit approaches to the solvent may offer a more realistic description of hydrogen bonding and solvation to reaction intermediates, but the corresponding capacitances also deviate from experimental values. These deviations highlight the need for a careful adjustment of parameters in order to reproduce not only solvation energies but also other physical properties of solid–liquid interfaces. Continuum approaches alone also necessarily do not capture local variations in the electric field from cations at the interface, which can affect the energetics of intermediates with substantial dipoles or polarizability. Finally, since the double-layer charge can be varied continuously, LPB/PCM models provide a way to determine electrochemical barriers at constant potential. However, double-layer charging and the atomic motion associated with reaction events occur on significantly different timescales. As a result, we suggest that more detailed approaches, such as the modified Poisson–Boltzmann model and/or the addition of a Stern layer, may be able to mitigate some but not all of the challenges discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Review of Computational Models for the Flow of Milled Biomass Part I: Discrete-Particle Models

Biomass is a renewable and sustainable energy resource. Current design of biomass handling and feeding equipment leverage both experiments and numerical modeling. This paper reviews the state-of-the-art discrete element methods (DEM) for the flow of milled biomass (Part I), accompanied by a comprehensive review on continuum-based computational models (Part II). The present review on DEM is primarily focused on the features and suitability of various particle shape models for different types of milled biomass because particle shape is the predominant attribute controlling the flow behavior of complex-shaped granular material. The general strengths and weaknesses in the applicability of those models for the milled biomass modeling are summarized. In particular, comments are provided to balance the numerical model capabilities and the computational cost for the development of DEM models. To our best knowledge, this is the first-of-its-kind review on DEM specifically for biomass. Our study indicates that the current DEM models require further development, calibration, and validation based on a deep understanding of biomass particle contact mechanics and experimental data support before they can be reliably used for predictive simulations in handling and feeding systems.

42 ENGINEERING↗
DOE OSTI2020

A Review of Computational Models for the Flow of Milled Biomass Part II: Continuum-Mechanics Models

The design of efficient material-handling systems for milled lignocellulosic biomass is challenging due to their complex particle morphologies and frictional interactions. Computational modeling, including the discrete element method (DEM) and continuum-based finite-element/volume methods, may offer scientific insight and predictive capabilities for the flow of milled biomass in hoppers and feeders. Herein, this article (Part II) presents a review of current state-of-the-art continuum models for the flow of milled biomass, whereas DEM models are reviewed in a companion article (Part I). Advances of numerical methods to solve the global governing equations are discussed first, followed by a comprehensive review of constitutive models for granular materials, including Drucker–Prager, hypoplastic, Cambridge-type, inertial-rheology, and nonlocal granular fluidity models. Specifically, we provide in-depth discussion on the suitability of those models for milled lignocellulosic biomass materials in terms of nonlinear elasticity, dependence of flow strength on pressure, density and shear rate, and compaction (dilation) associated with hardening (softening). Furthermore, our study shows that, despite the recent advances in continuum granular flow modeling, the most suitable constitutive models still need further development to account for material parametrization, multiflow regimes, and multiscale behavior before they can be reliably used to optimize the design and operation of biomass handling systems.

09 BIOMASS FUELS↗
DOE OSTI2020

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

Abstract With projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO 2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Cheng, Yanyan↗
DOE OSTI2020

Improving a Multilevel Turbulence Closure Model for a Shallow Lake in Comparison With Other 1-D Models

Lakes differ from lands in water availability, heat capacity, albedo, and roughness, which affect local surface-atmospheric interactions. This study modified a multilevel upper ocean model (UOM) for lake applications and evaluated its performance in Lake Taihu (China) with comprehensive measurements against three popular one-dimensional (1-D) lake models. These models were based on different concepts, including the self-similarity (FLake), the wind-driven eddy diffusion (LISSS), the k-ε turbulence closure (SIMSTRAT), and a simplified turbulence closure (UOM). The surface flux scheme in these models was unified to exclude the discrepancies in representing air-lake exchanges. All models in their default formulations presented obvious cold water temperature biases and largely underestimated the lake surface temperature (LST) diurnal range. For each model, these deficiencies were significantly reduced by incorporating new physics schemes or calibrated tunable parameters based on systematic sensitivity tests. The primary modifications for UOM included (1) a new scheme of decreased surface roughness lengths to better characterize the shallow lake, (2) a solar radiation penetration scheme with increased light extinction coefficient and surface absorption fraction to account for the high water turbidity, and (3) turbulent Prandtl number increased by a factor of 20 to reduce the turbulent vertical mixing. All other models were improved in these three aspects (roughness, extinction, and mixing) within their original formulations. Given these improvements, UOM showed superior performance to other models in capturing LST diurnal cycle and daily to seasonal variations, as well as summer-autumn vertical stratification changes. The new UOM is well suited for application in shallow lakes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Twisting the Hubbard model into the momentum-mixing Hatsugai–Kohmoto model

The Hubbard model is a standard theoretical tool for studying materials with strong electron–electron interactions, such as cuprate superconductors. Unfortunately, interaction-driven phenomena, such as a transition into the strongly correlated Mott insulator phase, are difficult to treat with established theoretical techniques. However, the exactly solvable Hatsugai–Kohmoto model displays similar Mott physics. In this work we show how the Hatsugai–Kohmoto model can be deformed continuously into the Hubbard model. The trick is to systematically reintroduce all the momentum mixing that the original Hatsugai–Kohmoto model omits. This can be accomplished by grouping n momenta into a cell and hybridizing them, resulting in the momentum-mixing Hatsugai–Kohmoto model. We recover the Bethe ansatz ground-state energy of the one-dimensional Hubbard model to within 1% from only ten mixed momenta. Overall, the convergence scales as 1/n2 as opposed to the inverse linear behaviour of standard finite-cluster techniques. Our results for a square lattice reproduce all the known features from state-of-the-art simulations also with only a few mixed momenta. Consequently, we believe that the momentum-mixing Hatsugai–Kohmoto model offers an alternative tool for strongly correlated quantum matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Wave speeds in the macroscopic extended model for ultrarelativistic gases

Equations determining wave speeds for a model of ultrarelativistic gases are investigated. This model is already present in literature; it deals with an arbitrary number of moments and it was proposed in the context of exact macroscopic approaches in Extended Thermodynamics. We find these results: the whole system for the determination of the wave speeds can be divided into independent subsystems which are expressed by linear combinations, through scalar coefficients, of tensors all of the same order; some wave speeds, but not all of them, are expressed by square roots of rational numbers; finally, we prove that these wave speeds for the macroscopic model are the same of those furnished by the kinetic model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Kitaev models based on unitary quantum groupoids

We establish a generalization of Kitaev models based on unitary quantum groupoids. In particular, when inputting a Kitaev-Kong quantum groupoid H{sub C}, we show that the ground state manifold of the generalized model is canonically isomorphic to that of the Levin-Wen model based on a unitary fusion category C. Therefore, the generalized Kitaev models provide realizations of the target space of the Turaev-Viro topological quantum field theory based on C.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Optimization of global model composed of radial basis functions using the term-ranking approach

A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Coarse-grained modeling of DNA curvature

The interaction of DNA with proteins occurs over a wide range of length scales, and depends critically on its local structure. In particular, recent experimental work suggests that the intrinsic curvature of DNA plays a significant role on its protein-binding properties. In this work, we present a coarse grained model of DNA that is capable of describing base-pairing, hybridization, major and minor groove widths, and local curvature. The model represents an extension of the recently proposed 3SPN.2 description of DNA [D. M. Hinckley, G. S. Freeman, J. K. Whitmer, and J. J. de Pablo, J. Chem. Phys. 139, 144903 (2013)], into which sequence-dependent shape and mechanical properties are incorporated. The proposed model is validated against experimental data including melting temperatures, local flexibilities, dsDNA persistence lengths, and minor groove width profiles.

Freeman, Gordon S.↗
DOE OSTI2015

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. We define such a non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. We test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. However, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while the corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. However, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Electronic transport in VO 2 —Experimentally calibrated Boltzmann transport modeling

Materials that undergo metal-insulator transitions (MITs) are under intense study because the transition is scientifically fascinating and technologically promising for various applications. Among these materials, VO 2 has served as a prototype due to its favorable transition temperature. While the physical underpinnings of the transition have been heavily investigated experimentally and computationally, quantitative modeling of electronic transport in the two phases has yet to be undertaken. In this work, we establish a density-functional-theory (DFT)-based approach to model electronic transport properties in VO 2 in the semiconducting and metallic regimes, focusing on band transport using the Boltzmann transport equations. To do so, we synthesized high quality VO 2 films and measured the transport quantities across the transition, in order to calibrate the free parameters in the model. We find that the experimental calibration of the Hubbard correction term can efficiently and adequately model the metallic and semiconducting phases, allowing for further computational design of MIT materials for desirable transport properties.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Comparison of fluid neutral models for one-dimensional plasma edge modeling with a finite volume solution of the Boltzmann equation

We derive fluid neutral approximations for a simplified 1D edge plasma model, suitable to study the neutral behavior close to the target of a nuclear fusion divertor, and compare its solutions to the solution of the corresponding kinetic Boltzmann equation. The plasma is considered as a fixed background extracted from a detached 2D simulation. We show that the Maxwellian equilibrium distribution is already obtained very close to the target, justifying the use of a fluid approximation. We compare three fluid neutral models: (i) a diffusion model; (ii) a pressure-diffusion model (i.e., a combination of a continuity and momentum equation) assuming equal neutral and ion temperatures; and (iii) the pressure-diffusion model coupled to a neutral energy equation taking into account temperature differences between neutrals and ions. Partial reflection of neutrals reaching the boundaries is included in both the kinetic and fluid models. We propose two methods to obtain an incident neutral flux boundary condition for the fluid models: one based on a diffusion approximation and the other assuming a truncated Chapman-Enskog distribution. The pressure-diffusion model predicts the plasma sources very well. The diffusion boundary condition gives slightly better results overall. Although including an energy equation still improves the results, the assumption of equal ion and neutral temperature already gives a very good approximation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Nonlinear Modeling of Ground Motions at Lotung LSST Site with Nested Surface Soil Constitutive Model

Seismic analyses of nuclear facilities require the use of validated numerical models that can realistically reproduce the response of soils during earthquakes. The nested surface nonlinear, hysteretic soil constitutive model is one of the soil constitutive models that is vastly used due to (i) lower number of free parameters compared to other nonlinear soil constitutive models, and (ii) because of the ease of calibrating these parameters using the commonly available soil data, i.e., G/G max and damping curves as a function of shear strain. This material model is available in commercial finite element software packages (LS-DYNA and Abaqus) as well in the open source finite element tool (Mastodon). The purpose of this study is to estimate the parameters required for this material model from the soil data available for the Lotung site and to demonstrate that this nonlinear soil constitutive model used in a time domain, finite element analysis can reasonably reproduce the actual measured soil motions recorded at Lotung during the LSST07 event on May 20, 1986. Results are introduced from all the three softwares mentioned above using the same material model.

42 ENGINEERING↗