Search NASA⌕ Search

SEARCH · Search NASA

Results for “Cluster Dynamics Simulations”

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

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

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗
DOE OSTI2026

Radiation Damage Mechanisms in Oxide-Based Solid Electrolytes

Ion implantation is a promising approach to mitigate dendrite formation in metal anode solid-state batteries by modifying the chemical and mechanical properties of oxide-based electrolyte surfaces such as Li 7 La 3 Zr 2 O 12 (LLZO). Yet, the detailed atomistic mechanisms and potentially adverse side effects of ion implantation remain poorly understood. In this study, we used molecular dynamics simulations to elucidate the evolution of radiation-induced damage in LLZO. The results reveal that radiation damage is primarily driven by recoils from heavy ions in the lattice, producing antisite defect clusters whose density decreases as recoil energy increases, a counterintuitive finding that highlights complex cascade fragmentation. Radiation-induced disruption to the crystal sublattice connectivity, especially in the La–O network critical for Li-ion transport, leads to degradation in ionic pathways at low energies, while higher energy cascades can form new conduction pathways via enhanced Zr–O network connectivity. These findings provide a mechanistic foundation for optimizing ion implantation strategies that balance disruption of the lattice for surface modification for dendrite resistance with preservation of essential ionic conductivity.

Defects↗
DOE OSTI2024

Impact of property covariance on cluster weak lensing scaling relations

ABSTRACT We present an investigation into a hitherto unexplored systematic that affects the accuracy of galaxy cluster mass estimates with weak gravitational lensing. Specifically, we study the covariance between the weak lensing signal, ΔΣ, and the ‘true’ cluster galaxy number count, Ngal, as measured within a spherical volume that is void of projection effects. By quantifying the impact of this covariance on mass calibration, this work reveals a significant source of systematic uncertainty. Using the MDPL2 simulation with galaxies traced by the SAGE semi-analytic model, we measure the intrinsic property covariance between these observables within the three-dimensional vicinity of the cluster, spanning a range of dynamical mass and redshift values relevant for optical cluster surveys. Our results reveal a negative covariance at small radial scales (R ≲ R200c) and a null covariance at large scales (R ≳ R200c) across most mass and redshift bins. We also find that this covariance results in a $2{\!-\!}3~{{\ \rm per\ cent}}$ bias in the halo mass estimates in most bins. Furthermore, by modelling Ngal and ΔΣ as multi-(log)-linear equations of secondary halo properties, we provide a quantitative explanation for the physical origin of the negative covariance at small scales. Specifically, we demonstrate that the Ngal–ΔΣ covariance can be explained by the secondary properties of haloes that probe their formation history. We attribute the difference between our results and the positive bias seen in other works with (mock)-cluster finders to projection effects. These findings highlight the importance of accounting for the covariance between observables in cluster mass estimation, which is crucial for obtaining accurate constraints on cosmological parameters.

Astronomy & Astrophysics↗
NASA NTRS1992

Simulation studies for surfaces and materials strength

Investigations were carried out in two major areas during the last reporting period. Energy- and structure-related properties of small gold clusters deposited on the GaAs(110) surface were investigated using a molecular dynamics procedure. Additionally, a comparative study of the many-body potentials of silicon systems was performed.

Halicioglu, Timur↗
NASA NTRS1995

Magnetohydrodynamic turbulence in the solar wind

The fluctuations in magnetic field and plasma velocity in solar wind, which possess many features of fully developed magnetohydrodynamic (MHD) turbulence, are discussed. Direct spacecraft observations from 0.3 to over 20 AU, remote sensing radio scintillation observations, numerical simulations, and various models provide complementary methods that show that the fluctuations in the wind parameters undergo significant dynamical evolution independent of whatever turbulence might exist in the solar photosphere and corona. The Cluster mission, with high time resolution particle and field measurements and its variable separation strategies, should be able to provide data for answering many questions on MHD turbulence.

Goldstein, Melvyn L.↗
NASA NTRS2022

Dayside Transient Phenomena and Their Impact on the Magnetosphere and Ionosphere

Dayside transients, such as hot flow anomalies, foreshock bubbles, magnetosheath jets, flux transfer events, and surface waves, are frequently observed upstream from the bow shock, in the magnetosheath, and at the magnetopause. They play a significant role in the solar wind-magnetosphere-ionosphere coupling. Foreshock transient phenomena, associated with variations in the solar wind dynamic pressure, deform the magnetopause, and in turn generates field-aligned currents (FACs) connected to the auroral ionosphere. Solar wind dynamic pressure variations and transient phenomena at the dayside magnetopause drive magnetospheric ultra low frequency (ULF) waves, which can play an important role in the dynamics of Earth’s radiation belts. These transient phenomena and their geoeffects have been investigated using coordinated in-situ spacecraft observations, spacecraft-borne imagers, ground-based observations, and numerical simulations. Cluster, THEMIS, Geotail, and MMS multi-mission observations allow us to track the motion and time evolution of transient phenomena at different spatial and temporal scales in detail, whereas ground-based experiments can observe the ionospheric projections of transient magnetopause phenomena such as waves on the magnetopause driven by hot flow anomalies or flux transfer events produced by bursty reconnection across their full longitudinal and latitudinal extent. Magnetohydrodynamics (MHD), hybrid, and particle-in-cell (PIC) simulations are powerful tools to simulate the dayside transient phenomena. This paper provides a comprehensive review of the present understanding of dayside transient phenomena at Earth and other planets, their geoeffects, and outstanding questions.

Bow shock↗
DOE OSTI2021

Dynamic Ride-Matching for Large-Scale Transportation Systems

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Rotational transition, domain formation, dislocations, and defects in vortex systems with combined sixfold and twelvefold anisotropic interactions

We introduce a phenomenological model for a pairwise repulsive interaction potential of vortices in a type-II superconductor, consisting of superimposed sixfold and twelvefold anisotropies. Using numerical simulations we study how the vortex lattice configuration varies as the magnitudes of the two anisotropic interaction terms change. A triangular lattice appears for all values, and rotates through 30 ° as the ratio of the sixfold and twlevefold anisotropy amplitudes is varied, in agreement with experimental results. The transition causes the vortex lattice to split into domains that have rotated clockwise or counterclockwise, with grain boundaries that are “decorated” by dislocations consisting of fivefold and sevenfold coordinated vortices. We also find intradomain dislocations and defects, and characterize them in terms of their energy cost. We discuss how this model could be generalized to other particle-based systems with anisotropic interactions, such as colloids, and consider the limit of very large anisotropy where it is possible to create cluster crystal states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system's response to potential hazardous events. This work adapts this approach to the traditional risk-based design process to synthetically generate hazardous modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the space of synthetic faults in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the hazard space.

Simulation↗
NASA NTRSDate not supplied

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system's response to potential hazardous events. This work adapts this approach to the traditional risk-based design process to synthetically generate hazardous modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the space of synthetic faults in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the hazard space.

Simulation↗
NASA NTRS1999

Evolution of Local Microstructures: Spatial Instabilities of Coarsening Clusters

This work examines the diffusional growth of discrete phase particles dispersed within a matrix. Engineering materials are microstructurally heterogeneous, and the details of the microstructure determine how well that material performs in a given application. Critical to the development of designing multiphase microstructures with long-term stability is the process of Ostwald ripening. Ripening, or phase coarsening, is a diffusion-limited process which arises in polydisperse multiphase materials. Growth and dissolution occur because fluxes of solute, driven by chemical potential gradients at the interfaces of the dispersed phase material, depend on particle size. The kinetics of these processes are "competitive," dictating that larger particles grow at the expense of smaller ones, overall leading to an increase of the average particle size. The classical treatment of phase coarsening was done by Todes, Lifshitz, and Slyozov, (TLS) in the limit of zero volume fraction, V(sub v), of the dispersed phase. Since the publication of TLS theory there have been numerous investigations, many of which sought to describe the kinetic scaling behavior over a 0 range of volume fractions. Some studies in the literature report that the relative increase in coarsening rate at low (but not zero) volume fractions compared to that predicted by TLS is proportional to v(sub v)(exp 1/2), whereas others suggcest V(sub v)(exp 1/3). This issue has been resolved recently by simulation studies at low volume fractions in three dimensions by members of the Rensselaer/MSFC team. Our studies of ripening behavior using large-scale numerical simulations suggest that although there are different circumstances which can lead to either scaling law, the most important length scale at low volume fractions is the diffusional analog of the Debye screening length. The numerical simulations we employed exploit the use of a recently developed "snapshot" technique, and identifies the nature of the coarsening dynamics at various volume fractions. Preliminary results of numerical and experimental investigations, focused on the growth of finite particle clusters, provide important insight into the nature of the transition between the two scaling regimes. The companion microgravity experiment centers on the growth within finite particle clusters, and follows the temporal dynamics driving microstructural evolution, using holography.

Frazier, Donald O.↗
DOE OSTI2023

Colloidal Clusters and Networks Formed by Oppositely Charged Nanoparticles with Varying Stiffnesses

Colloidal clusters and gels are ubiquitous in science and technology. Particle softness has a strong effect on interparticle interactions; however, our understanding of the role of this factor in the formation of colloidal clusters and gels is only beginning to evolve. Here, we report the results of experimental and simulation studies of the impact of particle softness on the assembly of clusters and networks from mixtures of oppositely charged polymer nanoparticles (NPs). Experiments were performed below or above the polymer glass transition temperature, at which the interaction potential and adhesive forces between the NPs were significantly varied. Hard NPs assembled in fractal clusters that subsequently organized in a kinetically arrested colloidal gel, while soft NPs formed dense precipitating aggregates, due to the NP deformation and the decreased interparticle distance. Importantly, interactions of hard and soft NPs led to the formation of discrete precipitating NP aggregates at a relatively low volume fraction of soft NPs. A phenomenological model was developed for interactions of oppositely charged NPs with varying softnesses. Furthermore, the experimental results were in agreement with molecular dynamics simulations based on the model. This work provides insight on interparticle interactions before, during, and after the formation of hard–hard, hard–soft, and soft–soft contacts and has impact for numerous applications of reversible colloidal gels, including their use as inks for additive manufacturing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Imaging plasma formation in isolated nanoparticles with ultrafast resonant scattering

We have recorded the diffraction patterns from individual xenon clusters irradiated with intense extreme ultraviolet pulses to investigate the influence of light-induced electronic changes on the scattering response. The clusters were irradiated with short wavelength pulses in the wavelength regime of different 4d inner-shell resonances of neutral and ionic xenon, resulting in distinctly different optical properties from areas in the clusters with lower or higher charge states. The data show the emergence of a transient structure with a spatial extension of tens of nanometers within the otherwise homogeneous sample. Simulations indicate that ionization and nanoplasma formation result in a light-induced outer shell in the cluster with a strongly altered refractive index. The presented resonant scattering approach enables imaging of ultrafast electron dynamics on their natural timescale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Single- and two-particle finite size effects in interacting lattice systems

Simulations of extended quantum systems are typically performed by extrapolating results of a sequence of finite-system-size simulations to the thermodynamic limit. In the quantum Monte Carlo community, twist-averaging was pioneered as an efficient strategy to eliminate one-body finite size effects. In the dynamical mean field community, cluster generalizations of the dynamical mean field theory were formulated to study systems with nonlocal correlations. In this work, we put the twist-averaging and the dynamical cluster approximation variant of the dynamical mean field theory onto equal footing, discuss commonalities and differences, and compare results from both techniques to the standard periodic boundary technique. At the example of Hubbard-type models with local, short-range and Yukawa-like longer range interactions we show that all methods converge to the same limit, but that the convergence speed differs in practice. We show that embedding theories are an effective tool for managing both one-body and two-body finite size effects, in particular if interactions are averaged over twist angles.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A two-phase substrate model for enzymatic hydrolysis of lignocellulose: application to batch and continuous reactors

Abstract Background Enzymatic hydrolysis continues to have a significant projected production cost for the biological conversion of biomass to fuels and chemicals, motivating research into improved enzyme and reactor technologies in order to reduce enzyme usage and equipment costs. However, technology development is stymied by a lack of accurate and computationally accessible enzymatic-hydrolysis reaction models. Enzymatic deconstruction of cellulosic materials is an exceedingly complex physico-chemical process. Models which elucidate specific mechanisms of deconstruction are often too computationally intensive to be accessible in process or multi-physics simulations, and empirical models are often too inflexible to be effectively applied outside of their batch contexts. In this paper, we employ a phenomenological modeling approach to represent rate slowdown due to substrate structure (implemented as two substrate phases) and feedback inhibition, and apply the model to a continuous reactor system. Results A phenomenological model was developed in order to predict glucose and solids concentrations in batch and continuous enzymatic-hydrolysis reactors from which liquor is continuously removed by ultrafiltration. A series of batch experiments were performed, varying initial conditions (solids, enzyme, and sugar concentrations), and best-fit model parameters were determined using constrained nonlinear least-squares methods. The model achieved a good fit for overall sugar yield and insoluble solids concentration, as well as for the reduced rate of sugar production over time. Additionally, without refitting model coefficients, good quantitative agreement was observed between results from continuous enzymatic-hydrolysis experiments and model predictions. Finally, the sensitivity of the model to its parameters is explored and discussed. Conclusions Although the phenomena represented by the model correspond to behaviors that emerge from clusters of mechanisms, and hence a set of model coefficients are unique to the substrate and the enzyme system, the model is efficient to solve and may be applied to novel reactor schema and implemented in computational fluid dynamics (CFD) simulations. Hence, this modeling approach finds the right balance between model complexity and computational efficiency. These capabilities have broad application to reactor design, scale-up, and process optimization.

09 BIOMASS FUELS↗
DOE OSTI2024

The Galaxy–Galaxy Strong Lensing Cross Section and the Internal Distribution of Matter in ΛCDM Substructure

Abstract Strong gravitational lensing offers a powerful probe of the detailed distribution of matter in lenses, while magnifying and bringing faint background sources into view. Observed strong lensing by massive galaxy clusters, which are often in complex dynamical states, has also been used to map their dark matter (DM) substructures on smaller scales. Deep high-resolution imaging has revealed the presence of strong lensing events associated with these substructures, namely galaxy-scale sub-halos. However, an inventory of these observed galaxy–galaxy strong lensing (GGSL) events is noted to be discrepant with state-of-the-art ΛCDM simulations. Cluster sub-halos appear to be over-concentrated compared to their simulated counterparts yielding an order-of-magnitude higher value of GGSL. In this paper, we explore the possibility of resolving this observed discrepancy by redistributing the mass within observed cluster sub-halos in ways that are consistent within the ΛCDM paradigm of structure formation. Lensing mass reconstructions from data provide constraints on the mass enclosed within apertures and are agnostic to the detailed mass profile within them. Therefore, as the detailed density profile within cluster sub-halos currently remains unconstrained by data, we are afforded the freedom to redistribute the enclosed mass. We investigate if rearranging the mass to a more centrally concentrated density profile helps alleviate the GGSL discrepancy. We report that refitting cluster sub-halos to the ubiquitous ΛCDM-motivated Navarro–Frenk–White profile, and further modifying them to include significant baryonic components, does not resolve this tension. A resolution to this persisting GGSL discrepancy may require more careful exploration of alternative DM models.

Astronomy & Astrophysics↗
DOE OSTI2020

Effect of Collective Dynamics and Anharmonicity on Entropy in Heterogenous Catalysis: Building the Case for Advanced Molecular Simulations

In this paper, we present a perspective on the computational determination of entropy and its effects and consequences on heterogeneous catalysis. Special attention is paid to the role of anharmonicity and the deviations from the standard harmonic oscillator approximations which can fail to provide a reliable assessment of entropy. To address these challenges, advanced methodologies are needed that can explicitly account for these thermodynamic drivers through the appropriate statistical sampling of reactive free energy surfaces. We discuss where anharmonicity should be expected, where it has been observed from a theoretical perspective, and the methods currently employed to address it. We concentrate on three types of systems where we have observed major, nonnegligible anharmonic effects: (1) supported nanoparticles, where the migration of metal atoms, complexes, and entire clusters exhibit anharmonic behavior in their dynamic motion; (2) porous solids where confinement effects distort potential energy surfaces and hinder molecular motions resulting in large entropic terms; and (3) solid/liquid interfaces, where interactions between solvent molecules and adsorbed species can result in large solvent organization free energy and unique reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Elucidating size effects on the yield strength of single-crystal Cu via the Richtmyer–Meshkov instability

Capturing the dynamic response of a material under high strain-rate deformation often demands challenging and time consuming experimental effort. While shock hydrodynamic simulation methods can aid in this area, a priori characterizations of the material strength under shock loading and spall failure are needed in order to parameterize constitutive models needed for these computational tools. Moreover, parameterizations of strain-rate-dependent strength models are needed to capture the full suite of Richtmyer–Meshkov instability (RMI) behavior of shock compressed metals, creating an unrealistic demand for these training data solely on experiments. Herein, we sweep a large range of geometric, crystallographic, and shock conditions within molecular dynamics (MD) simulations and demonstrate the breadth of RMI in Cu that can be captured from the atomic scale. In this work, yield strength measurements from jetted and arrested material from a sinusoidal surface perturbation were quantified as Y RMI = 0.787 ± 0.374 GPa, higher than strain-rate-independent models used in experimentally matched hydrodynamic simulations. Defect-free, single-crystal Cu samples used in MD will overestimate Y RMI , but the drastic scale difference between experiment and MD is highlighted by high confidence neighborhood clustering predictions of RMI characterizations, yielding incorrect classifications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗