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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Effect of double layers on magnetosphere-ionosphere coupling

The Earth's auroral zone contains dynamic processes occurring on scales from the length of an auroral zone field line which characterizes Alfven wave propagation to the scale of microscopic processes which occur over a few Debye lengths. These processes interact in a time-dependent fashion since the current carried by the Alfven waves can excite microscopic turbulence which can in turn provide dissipation of the Alfven wave energy. This review will first describe the dynamic aspects of auroral current structures with emphasis on consequences for models of microscopic turbulence. A number of models of microscopic turbulence will be introduced into a large-scale model of Alfven wave propagation to determine the effect of various models on the overall structure of auroral currents. In particular, the effects of a double layer electric field which scales with the plasma temperature and Debye length is compared with the effect of anomalous resistivity due to electrostatic ion cyclotron turbulence in which the electric field scales with the magnetic field strength. It is found that the double layer model is less diffusive than in the resistive model leading to the possibility of narrow, intense current structures.

Lysak, Robert L.↗
DOE OSTI2024

Surface Dynamics of Selective Hydration of Rutile TiO 2 : A Kinetic Monte Carlo Approach

The utility of atomic layer deposition (ALD) continues to expand beyond conformal thin-film fabrication to include area- or site-selective ALD. We previously identified a strategy for site-selective ALD (SS-ALD) via the evaluation of thermodynamically preferred hydration of rutile TiO 2 surfaces, as deduced from electronic structure calculations. Here, we report a novel kinetic Monte Carlo (KMC) model that allows for the investigation of surface dynamics and kinetics that improves our understanding of and intuition for the selective hydration strategy. Here we demonstrate the validity of the strategy with respect to step-edge defects for the lowest energy (110) facet as well as report results for the other common facets which agree with experimental STM observations. The results here indicate that the selective hydration strategy is feasible both thermodynamically (evaluated in our previous publication) and kinetically (from the KMC model). Because diffusion has a slower rate than others, we find that any proximity effects between terrace and defect sites are unlikely to affect the selective hydration strategy for rutile TiO 2 . The KMC model further provides relevant timescales for achieving selectivity experimentally and establishes the kinetic viability of the selective hydration approach to SS-ALD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Towards understanding the magnetic properties of the breathing pyrochlore compound Ba3Yb2Zn5O11through single-crystal studies

Abstract Ba 3 Yb 2 Zn 5 O 11 is exceptional among breathing pyrochlore compounds for being in the nearly-decoupled limit where inter-tetrahedron interactions are weak, hosting isolated clusters or molecular magnet-like tetrahedra of magnetic ytterbium (Yb 3+ ) ions. In this work, we present the study carried out on single-crystal samples of the breathing pyrochlore Ba 3 Yb 2 Zn 5 O 11 , using a variety of magnetometry and neutron scattering techniques along with theoretical modeling. We employ inelastic neutron scattering to investigate the magnetic dynamics as a function of applied field (with respect to both magnitude and direction) down to a temperature of 70 mK, where inelastic scattering reveals dispersionless bands of excitations as found in earlier powder sample studies, in good agreement with a single-tetrahedron model. However, diffuse neutron scattering at zero field and dc-susceptibility at finite field exhibit features suggesting the presence of excitations at low-energy that are not captured by the single tetrahedron model. Analysis of the local structure down to 2 K via pair distribution function analysis finds no evidence of structural disorder. We conclude that effects beyond the single tetrahedron model are important in describing the low-energy, low-temperature physics of Ba 3 Yb 2 Zn 5 O 11 , but their nature remains undetermined.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Domain Decomposition Model Reduction Method for Linear Convection-Diffusion Equations with Random Coefficients

We developed a domain decomposition model reduction method for linear steady- state convection-diffusion equations with random coefficients. Of particular interest to this effort are the diffusion equation with random diffusivity and the convection-dominated transport equation with random velocity. We investigated two types of random fields, i.e., colored noises and discrete white noises, both of which can lead to high-dimensional parametric dependence in practice. The motivation is to exploit domain decomposition to reduce the parametric dimension of local problems in subdomains, such that an entire parametric map can be approximated with a small number of expensive partial differential equation (PDE) simulations. The new method combines domain decomposition with model reduction and sparse polynomial approximation, so as to simultaneously handle the high dimensionality and irregular behavior of the PDEs under consideration. Here, the advantages of our method lie in three aspects: (i) online-offline decomposition, i.e., the online cost is independent of the size of the triangle mesh; (ii) sparse approximation of operators involving nonaffine high-dimensional random fields; (iii) an effective strategy to capture irregular behaviors, e.g., sharp transitions of the PDE solutions. Two numerical examples are provided to demonstrate the advantageous performance of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Mathematical and Numerical Analyses of Peridynamics for Multiscale Materials Modeling

We have treated the modeling, analysis, numerical analysis, and algorithmic development for nonlocal models of diffusion and mechanics. Variational formulations were developed and finite element methods were developed based on those formulations for both steady state and time dependent problems. Obstacle problems and optimization problems for the nonlocal models were also treated and connections made with fractional derivative models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A sub-pore model for multi-scale reaction–diffusion problems in porous media

Applications of reaction–diffusion systems in porous media pose a challenging problem for computational modeling approaches due to their multi-physics and multi-scale nature. The length scales usually span 3–4 orders of magnitude while physical phenomena involved include heat and mass transfer processes, and surface reactions. In this paper, a novel methodology that accounts for all the length scales and physical phenomena involved in a single framework is described. A length scale based dual approach is proposed – the larger pore channels (macro-pores) are resolved using conventional numerical techniques and a novel ‘sub-pore’ model is used to account for the unresolved pore channels (sub-pores) and the important physics therein. The porous network in the sub-pore system is composed of a fractal-like hierarchical system of straight cylindrical pores. Simplified governing equations for mass and energy transport are solved within the sub-pore system along with a reaction kinetics model to account for surface adsorption. An implicit coupling strategy is used to couple the macro-pore and the sub-pore systems so as to ensure conservation. The developed methodology is then applied to a few test cases and it is established that the proposed framework is necessary for problems where the adsorption time scale is much smaller than (diffusion-limited) or comparable to the diffusion time scale. Furthermore, it is also demonstrated that the framework can be potentially used to model the network of porous channels in its entirety thus significantly reducing computational costs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient Reformulation of Linear and Nonlinear Solid-Phase Diffusion in Lithium-ion Battery Models using Symmetric Polynomials: Mass Conservation and Computational Efficiency

Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms, along with suitable discretization or approximations for the solid-phase diffusion equation. The solid-phase diffusion equation represents the main computational burden for typical pseudo-2-dimensional (p2D) models since these equations in the pseudo r -dimension must be solved at each point in the computational grid. This substantially increases the complexity of the model as well as the computational time. Traditional approaches towards simplifying solid-phase diffusion possess certain significant limitations, especially in modeling emerging electrode materials which involve phase changes and variable diffusivities. A computationally efficient representation for solid-phase diffusion is discussed in this paper based on symmetric polynomials using Orthogonal Collocation and Galerkin formulation (weak form). A systematic approach is provided to increase the accuracy of the approximation (p form in finite element methods) to enable efficient simulation with a minimal number of semi-discretized equations, ensuring mass conservation even for non-linear diffusion problems involving variable diffusivities. These methods are then demonstrated by incorporation into the full p2D model, illustrating their advantages in simulating high C-rates and short-time dynamic operation of Lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

A Method for Obtaining Liquid–Solid Adsorption Rates from Molecular Dynamics Simulations: Applied to Methanol on Pt(111) in H 2 O

Adsorption is an important step in heterogeneous catalysis as it predetermines how many reactant molecules can participate in a surface reaction per unit time. While the rate of adsorption processes is well studied in gas–solid adsorption in both theory and experiment, such rates are still not well studied for liquid–solid adsorption. This is partly because the ever-changing configurations of liquid-phase solvent molecules impede the ability to study a molecule approaching a surface from a liquid phase by either experiment or theory. In this work, we develop a method using molecular dynamics (MD) simulations to study the rate of adsorption in liquid–solid adsorption processes. Specifically, we use MD to model the diffusion of a methanol molecule in aqueous solvent and its adsorption to a Pt(111) surface. We find that by approximating the solute motion as following the same displacement rates as a random walk model, the adsorbed and non-adsorbed states of the methanol molecule near the Pt(111) surface can be discerned and quantified. In particular, this methodology enables extracting a sticking coefficient and a macroscopically relatable adsorption rate. This method can be applied to arbitrary types of reactants and surfaces, as well as different liquid environments, thus providing a general tool for predicting quantitative adsorption rates of liquid–solid adsorption systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1999

Multi-Dimensional Quantum Tunneling and Transport Using the Density-Gradient Model

We show that quantum effects are likely to significantly degrade the performance of MOSFETs (metal oxide semiconductor field effect transistor) as these devices are scaled below 100 nm channel length and 2 nm oxide thickness over the next decade. A general and computationally efficient electronic device model including quantum effects would allow us to monitor and mitigate these effects. Full quantum models are too expensive in multi-dimensions. Using a general but efficient PDE solver called PROPHET, we implemented the density-gradient (DG) quantum correction to the industry-dominant classical drift-diffusion (DD) model. The DG model efficiently includes quantum carrier profile smoothing and tunneling in multi-dimensions and for any electronic device structure. We show that the DG model reduces DD model error from as much as 50% down to a few percent in comparison to thin oxide MOS capacitance measurements. We also show the first DG simulations of gate oxide tunneling and transverse current flow in ultra-scaled MOSFETs. The advantages of rapid model implementation using the PDE solver approach will be demonstrated, as well as the applicability of the DG model to any electronic device structure.

Biegel, Bryan A.↗
DOE OSTI2025

Ab Initio Design of High-Entropy Thermal/ Environmental Barrier Coatings

Next generation thermal/environmental barrier coatings (TEBC) require carefully balancing various properties including phase stability, thermal conductivity, coefficient of thermal expansion (CTE), mechanical properties, and resistance against hot corrosion and water vapor recession. This work mainly focuses on rapid design of cost-effective high entropy rare-earth disilicates and aluminum garnets to protect SiC-based ceramic matrix composites and nickel-based superalloys in the hot section of gas turbine engines using density functional theory methods. Our calculations identify several low-cost high entropy TEBC exhibiting ultralow thermal conductivity at 1500 K and desirable CTE while maintaining good mechanical properties, including Er1/2Y3/4Yb3/4Si2O7, Gd1/4Er1/4Y3/4Yb3/4Si2O7, Eu1/4Er1/4Y3/4Yb3/4Si2O7, and (Y1/4Gd1/4Er1/4Yb1/4)3Al5O12. This work also aims to gain fundamental understanding of oxygen diffusion in model disilicates. Minimizing oxidizer (such as water vapor and oxygen) permeability through the EBC layer can significantly decrease the growth rate of thermally grown oxide and extend the service life of the coating system. Oxygen diffusion mechanisms including formation energy of defects under varying oxygen conditions and defect migration energy barriers will be presented.

coefficient of thermal expansion↗
NASA NTRS1995

Vibrationally excited H2, HCl, and NO(+) in the diffuse clouds toward Zeta Ophiuchi

Absorption lines from vibrationally excited H2 and from HCl were detected in the ultraviolet spectrum of Zeta Oph with the Hubble Space Telescope (HST). Improved upper limits on NO(+) absorption were obtained as well. The data were analyzed with an updated version of a chemical model for diffuse clouds (van Dishoeck & Black 1986) as a guide to understanding this gaseous environment. The comparisons suggest that the flux of ultraviolet radiation impinging on the cloud surface is approximately 1-2 times the average interstellar radiation field, which is lower than once believed.

Federman, S. R.↗
NASA NTRS2003

The Chemical Composition and Gas-to-Dust Mass Ratio of Nearby Interstellar Matter

We use recent results on interstellar gas toward nearby stars and interstellar by-products within the solar system to select among the equilibrium radiative transfer models of the nearest interstellar material presented in Slavin & Frisch. For the assumption that O/H - 400 parts per million, models 2 and 8 are found to yield good fits to available data on interstellar material inside and outside of the heliosphere, with the exception of the Ne abundance in the pickup ion and anomalous cosmic-ray populations. For these models, the interstellar medium (ISM) at the entry point to the heliosphere has n(H(sup 0)) = 0.202-0.208/cu cm, n(He(sup 0) = 0.0137-0.0152/cu cm, and ionizations X(H) = 0.29-0.30, X(He) = 0.47-0.51. These best models suggest that the chemical composition of the nearby ISM is approx.60%-70% subsolar if S is undepleted. Both H(0) and H(+) need to be included when evaluating abundances of ions found in warm diffuse clouds. Models 2 and 8 yield an H filtration factor of approx.0.46. Gas-to-dust mass ratios for the ISM toward epsilon CMa are R(sub gd) = 178-183 for solar abundances of Holweger or R(sub gd) = 611-657 for an interstellar abundance standard 70% solar. Direct observations of dust grains in the solar system by Ulysses and Galileo yield R(sub gd) appr0x. 115 for models 2 and 8, supporting earlier results (Frisch and coworkers). If the local ISM abundances are subsolar, then gas and dust are decoupled over small spatial scales. The inferred variation in R(sub gd) over parsec length scales is consistent with the fact that the ISM near the Sun is part of a dynamically active cluster of cloudlets flowing away from the Sco-Cen association. Observations toward stars within approx.500 pc show that R(sub gd) correlates with the percentage of the dust mass that is carried by iron, suggesting that an Fe-rich grain core (by mass) remains after grain destruction. Evidently large dust grains (>10(exp -13) g) and small dust grains (<10(exp -13) g) are not well mixed over parsec length spatial scales in the ISM. It also appears that very small C-dominated dust grains have been destroyed in the ISM within several parsecs of the Sun, since C appears to be essentially undepleted. However, if gas-dust coupling breaks down over the cloud lifetime, the missing mass arguments applied here to determine R(sub gd) and dust grain mineralogy are not appropriate.

Frisch, Priscilla C.↗
DOE OSTI2019

Studying Nanosecond Discharge in Argon at Atmospheric Pressure with Preionization

The influence of initial conditions on specific features of the formation and development of a cathode-directed ionization wave between two flat electrodes in argon at atmospheric pressure was studied at nanosecond time resolution using a high-speed photoelectron monitor and numerical simulations based on the two-dimensional axisymmetric diffusion–drift model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Printable organic thin film transistors for glucose detection incorporating inkjet-printing of the enzyme recognition element

The effect of device architecture upon the response of printable enzymatic glucose sensors based on poly(3-hexythiophene) (P3HT) organic thin film transistors is presented. The change in drain current is used as the basis for glucose detection and we show that significant improvements in drain current response time can be achieved by modifying the design of the sensor structure. In particular, we show that eliminating the dielectric layer and reducing the thickness of the active layer reduce the device response time considerably. The results are in good agreement with a diffusion based model of device operation, where an initial rapid dedoping process is followed by a slower doping of the P3HT layer from protons that are enzymatically generated by glucose oxidase (GOX) at the Nafion gate electrode. The fitted diffusion data are consistent with a P3HT doping region that is close to the source-drain electrodes rather than located at the P3HT:[Nafion:GOX] interface. Finally, we demonstrate that further improvements in sensor structure and morphology can be achieved by inkjet-printing the GOX layer, offering a pathway to low-cost printed biosensors for the detection of glucose in saliva.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Iterative spectral method for solving electrostatic or magnetostatic problems in complex and evolving heterostructures

Electrostatic or magnetostatic problems involving complex heterogeneity are nontrivial for modeling and simulation. Most existing numerical methods focus on sharp interface models and the computational cost increases with increasing complexity of the geometry. Here we develop an iterative spectral method, the bound charge successive approximation algorithm, to solve electrostatic or magnetostatic heterogeneity problems in the context of diffuse-interface modeling. As tests and verifications, this algorithm is applied to calculation of the depolarization factor of an ellipsoid, and simulation of random dielectric mixtures and the dielectophoretic motion of multiple particles. The algorithm shows excellent efficiency and the computational cost mainly depends on the permittivity or permeability contrast in the whole system, regardless of the complexity of the geometry. Specifically, for evolving heterostructures the solution of bound charge in one time step can be used as input for the next, which could further significantly shorten the iteration (approximation) process, making it practical to simulate the long-range electrostatic or magnetostatic interaction in complex and evolving heterostructures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Reproducing curvature effects due to differential diffusion in tabulated chemistry for premixed flames

Tabulated chemistry reduces the cost of modeling reacting flows by transporting fewer scalars. It also has the benefit of introducing fewer terms that must be closed for Reynolds-averaged Navier-Stokes and large eddy simulation modeling. In this study of premixed lean hydrogen–air flames, an extension to an existing tabulated chemistry model was developed by including differential diffusion and thermal diffusion of multiple species, using a mixture-averaged diffusion model. The transport equations for the tabulation variables (a progress variable and a mixture fraction-like variable) have been formulated, and curvature effects are intrinsic to the transport equations derived from first principles. The mathematical derivation is general, such that other fuel/air mixtures could be examined. The new tabulated chemistry model was then validated using a range of configurations, including flames with curvature, extinction, and hot spots. The model validation indicated excellent agreement with detailed chemistry simulations for lean premixed hydrogen–air mixtures. Conditional means of the progress variable source term were shown to agree well with detailed chemistry simulations of two-dimensional freely propagating flames and three-dimensional turbulent flames. Finally, progress variable source terms three times higher than one-dimensional flat flame results were identified and validated with detailed chemistry, and super-adiabatic hot spots were also predicted in the turbulent flame configuration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗