Search NASA⌕ Search

SEARCH · Search NASA

Results for “PARTICLE INTERACTIONS”

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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Thermodynamic stability and structural properties of cluster crystals formed by amphiphilic dendrimers

We pursue the goal of finding real-world examples of macromolecular aggregates that form cluster crystals, which have been predicted on the basis of coarse-grained, ultrasoft pair potentials belonging to a particular mathematical class [B. M. Mladek et al., Phys. Rev. Lett. 46, 045701 (2006)]. For this purpose, we examine in detail the phase behavior and structural properties of model amphiphilic dendrimers of the second generation by means of monomer-resolved computer simulations. On augmenting the density of these systems, a fluid comprised of clusters that contain several overlapping and penetrating macromolecules is spontaneously formed. Upon further compression of the system, a transition to multi-occupancy crystals takes place, the thermodynamic stability of which is demonstrated by means of free-energy calculations, and where the FCC is preferred over the BCC-phase. Contrary to predictions for coarse-grained theoretical models in which the particles interact exclusively by effective pair potentials, the internal degrees of freedom of these molecules cause the lattice constant to be density-dependent. Furthermore, the mechanical stability of monodisperse BCC and FCC cluster crystals is restricted to a bounded region in the plane of cluster occupation number versus density. The structural properties of the dendrimers in the dense crystals, including their overall sizes and the distribution of monomers are also thoroughly analyzed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Hierarchical modeling of molecular energies using a deep neural network

In this work, we introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. Finally, with minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Turbulence-driven anisotropic electron tail generation during magnetic reconnection

Magnetic reconnection (MR) plays an important role in particle transport, energization, and acceleration in space, astrophysical, and laboratory plasmas. In the Madison Symmetric Torus reversed field pinch, discrete MR events release large amounts of energy from the equilibrium magnetic field, a fraction of which is transferred to electrons and ions. Previous experiments revealed an anisotropic electron tail that favors the perpendicular direction and is symmetric in the parallel. New profile measurements of x-ray emission show that the tail distribution is localized near the magnetic axis, consistent modeling of the bremsstrahlung emission. The tail appears first near the magnetic axis and then spreads radially, and the dynamics in the anisotropy and diffusion are discussed. The data presented imply that the electron tail formation likely results from a turbulent wave-particle interaction and provides evidence that high energy electrons are escaping the core-localized region through pitch angle scattering into the parallel direction, followed by stochastic parallel transport to the plasma edge. Finally, new measurements also show a strong correlation between high energy x-ray measurements and tearing mode dynamics, suggesting that the coupling between core and edge tearing modes is essential for energetic electron tail formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Collisional enhancement of energetic particle Alfvénic resonance width in tokamaks

The phase-space structure of resonances between fast ions and an Alfvénic mode and the associated modification of density profiles in tokamaks are studied as a function of particle collisions. Guiding-center simulations in a realistic tokamak equilibrium are employed to address the resonance broadening parametric dependencies with respect to changes in the pitch-angle scattering rate. The rate of collisional replenishment, along with resonance strength, given by the combination of eigenmode and resonance structures and equilibrium parameters, determines saturation amplitudes for a given damping rate. As seen from the distribution function flattening, collisions have an effect of broadening the resonances, while the absolute value of δf decreases with increasing collisionality. It is observed that the collisional broadening can be comparable to the collisionless resonance width due to the mode amplitude alone. The resonance broadening coefficients are compared with the existing theory based on analytically expected saturation levels, showing fair agreement. Furthermore the results can be useful in assisting reduced kinetic models, such as quasilinear models, when prescribing the effective resonance phase-space width, i.e., the mode-particle interaction platform, due to collisional or turbulent processes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Characterization of LiFePO4 samples obtained by pulse combustion under various conditions of synthesis

Lithium iron phosphate (LiFePO4, LFP) is one of the widely used cathode materials for rechargeable lithium ion batteries. LFP batteries are widely used for electric vehicles and backup power due to their important advantages such as low cost, lifetime, efficiency, and reliability. There are still several technical challenges that need to be addressed: The increase of energy density or further reduction of their final cost. This paper concerned with the characterization of carbon coated LiFePO4 nanopowder cathode materials produced under different conditions by pulse combustion for providing energy to the reactor for the synthesis. The reactor was built according to the principles of the thermoacoustic burner on the basis of the Helmholtz resonator. The investigated nanopowders are synthesized by complete and incomplete combustion and calcined at 700 °C. The obtained samples were characterized by X-ray diffraction, Fourier transform infrared, Raman, and Mössbauer spectroscopy. Observed low-Temperature magnetic phase transitions definitively identified the crystal phases. The morphology of samples was controlled by scanning electron microscopy. The aim of this work is to show that it is possible to achieve a desired crystal phase by pulse combustion in a relatively cheap and fast way. The extremely rapid synthesis of almost pure phase material is possible due to the reduction in size of interacting particles and to an enormous number of collisions between them as a result of strong turbulent flow associated with explosive combustion. © 2019 Author(s).

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

Mean force kinetic theory applied to self-diffusion in supercritical Lennard-Jones fluids

A new kinetic-theory-based calculation of the self-diffusion coefficient for dense supercritical Lennard-Jones fluids is presented. The mean force kinetic theory, which was recently developed for transport in dense plasmas, is applied for the calculation of diffusion in dense neutral fluids. The calculation only requires the pair distribution function, a quantity that is readily calculable from equilibrium statistical mechanics for many systems, including the Lennard-Jones fluid. The self-diffusion coefficients are compared with calculations from molecular dynamics simulations, and good agreement at high density is demonstrated, even in the vicinity of the solid–fluid coexistence line. A comparison of different kinetic models with molecular dynamics simulations demonstrates that the transport coefficients have important contributions due to particle interaction via a potential of mean force and local correlations, which increase the collision rate. The new calculations compare well to those from free-volume theory and overcome a limitation of this theory that prevents its use in systems that interact via long range monotonic potentials. It is expected that this approach will also apply to other systems, including neutral-plasma and neutral-electrolyte mixtures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Simulating sticky particles: A Monte Carlo method to sample a stratification

Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.

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

From predicting to learning dissipation from pair correlations of active liquids

Active systems, which are driven out of equilibrium by local non-conservative forces, can adopt unique behaviors and configurations. Towards designing such materials, an important challenge is to precisely connect the static structure of active systems to the dissipation of energy induced by the local driving. Here, we use tools from liquid-state theories and machine learning to take on these challenges. We first demonstrate analytically for an isotropic active matter system that dissipation and pair correlations are closely related when driving forces behave like an active temperature. We then extend a nonequilibrium mean-field framework for predicting these pair correlations which, unlike most existing approaches, is applicable even for strongly interacting particles and far from equilibrium, to predict dissipation in these systems. Based on this theory, we reveal analytically a robust relation between dissipation and structure which holds even as the system approaches a nonequilibrium phase transition. Finally, we construct a neural network which maps static configurations of particles to their dissipation rate without any prior knowledge of the underlying dynamics. Furthermore, our results open novel perspectives on the interplay between dissipation and organization out-of-equilibrium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantum computing for fusion energy science applications

This is a review of recent research exploring and extending present-day quantum computing capabilities for fusion energy science applications. We begin with a brief tutorial on both ideal and open quantum dynamics, universal quantum computation, and quantum algorithms. Then, we explore the topic of using quantum computers to simulate both linear and nonlinear dynamics in greater detail. Because quantum computers can only efficiently perform linear operations on the quantum state, it is challenging to perform nonlinear operations that are generically required to describe the nonlinear differential equations of interest. In this work, we extend previous results on embedding nonlinear systems within linear systems by explicitly deriving the connection between the Koopman evolution operator, the Perron–Frobenius evolution operator, and the Koopman–von Neumann evolution (KvN) operator. We also explicitly derive the connection between the Koopman and Carleman approaches to embedding. Extension of the KvN framework to the complex-analytic setting relevant to Carleman embedding, and the proof that different choices of complex analytic reproducing kernel Hilbert spaces depend on the choice of Hilbert space metric are covered in the appendixes. Finally, we conclude with a review of recent quantum hardware implementations of algorithms on present-day quantum hardware platforms that may one day be accelerated through Hamiltonian simulation. We discuss the simulation of toy models of wave–particle interactions through the simulation of quantum maps and of wave–wave interactions important in nonlinear plasma dynamics.

Joseph, I. (ORCID:0000000255400840)↗
DOE OSTI2023

Lightweight and effective tensor sensitivity for atomistic neural networks

Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here, we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 $\frac{\text{kcal}}{\text{mol}}$ for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature.

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

Direct numerical simulation of open-channel flow over a heterogeneous particle bed at low relative submergence

In this article, this study investigates turbulent open-channel flows over beds of irregularly arranged particles, using direct numerical simulations at a friction Reynolds number of Re T = 300⁠. Two distinct cases are examined: a polydisperse bed (P800) composed of multiple layers of randomly distributed spheres of varying sizes, and a monodisperse bed (M1015) formed by a random distribution of uniform sized spheres, with a bottommost single layer of varied-sized particles to introduce realistic randomness. Our investigation unveils a rich network of low- and high-speed streaks within the flow field, exhibiting distinctive behaviors in different bed configurations. The P800 case presents a poorly organized flow pattern induced by the varied particle sizes and arrangements, while the M1015 case shows a more regular flow pattern, marked by larger streaks. We also observe that total wall shear stress is substantially influenced by surface roughness-induced drag, extending beyond the effects documented in existing studies of open-channel flows. The present study reveals intricate secondary flow patterns over irregular particle beds. Large-scale circulations are discerned around particle crests in the P800 case and localized circulations with increased turbulence in the M1015 case. Furthermore, analysis of Reynolds stress tensor components indicates that roughness disrupts coherent turbulent eddies, consequently mitigating peak stress. We quantify correlations between drag force and local fluid velocity fluctuations. Notably, a larger deviation in drag is observed in the P800 case compared to M1015, accentuating the influence of particle size and distribution on fluid–particle interactions.

42 ENGINEERING↗
DOE OSTI2026

Simulation of electron Bernstein waves using FullWave with a 2D non-local hot plasma model

Hot plasma wave simulation capability is expanded in the FullWave code by updating the hybrid iterative solver in the code with a semi-implicit time stepping method. The new approach is used to simulate Electron Bernstein Wave (EBW) heating in over-dense spherical tokamak plasmas. The code’s hybrid iterative solver circumvents the prohibitive memory cost of direct methods by combining a time evolution of Maxwell’s equations with frequency-domain relaxation, while the conductivity kernel, calculated via 3D particle tracking, captures the essential non-local wave–particle interactions. One-dimensional EBW simulations verify the algorithm’s accuracy by demonstrating mode conversion from X-mode wave to EBW at the upper hybrid resonance and a strong cyclotron damping near the plasma core. Two-dimensional simulation reproduces the predicted short EBW wavelength and quantitatively matches the hot-plasma dispersion relation. This study demonstrates the fidelity of the hybrid solver for the electron cyclotron frequency range.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Size-dependent dynamic structures of supported gold nanoparticles in CO oxidation reaction condition

Significance Gold is the noblest metal. However, when the size decreases to nanoscale and is supported on reducible oxides, the gold nanoparticle shows exceptionally high catalytic performance even at low temperatures. Here, through state-of-the-art in situ aberration-corrected environmental transmission electron microscopy and ab initio molecular-dynamic simulations, we discovered that, upon exposing to reactant gas (carbon monoxide and oxygen), ultrasmall gold clusters on ceria show a size-dependent order-to-disorder transformation with generation of dynamic low-coordinated atoms, which presumably can effectively boost the oxidation reaction of carbon monoxide. The findings provide much-needed insights on the origin of size-dependent catalytic properties of supported gold and demonstrate a size effect in absorbent–particle interactions that may widely exist and play an essential role in heterogeneous catalysts.

He, Yang↗
DOE OSTI2019

Crumpling of silver nanowires by endolysosomes strongly reduces toxicity

Fibrous particles interact with cells and organisms in complex ways that can lead to cellular dysfunction, cell death, inflammation, and disease. The development of conductive transparent networks (CTNs) composed of metallic silver nanowires (AgNWs) for flexible touchscreen displays raises new possibilities for the intimate contact between novel fibers and human skin. Here, we report that a material property, nanowire-bending stiffness that is a function of diameter, controls the cytotoxicity of AgNWs to nonimmune cells from humans, mice, and fish without deterioration of critical CTN performance parameters: electrical conductivity and optical transparency. Both 30- and 90-nm-diameter AgNWs are readily internalized by cells, but thinner NWs are mechanically crumpled by the forces imposed during or after endocytosis, while thicker nanowires puncture the enclosing membrane and release silver ions and lysosomal contents to the cytoplasm, thereby initiating oxidative stress. This finding extends the fiber pathology paradigm and will enable the manufacture of safer products incorporating AgNWs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials

The rigid constraints of chemistry-dictated by quantum mechanics and the discrete nature of the atom-limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.

crystal structures↗
DOE OSTI2015

Landau resonant modification of multiple kink mode contributions to 3D tokamak equilibria

Detailed measurements of the plasma's response to applied magnetic perturbations provide experimental evidence that the form of three-dimensional (3D) tokamak equilibria, with toroidal mode number n = 1, is determined by multiple stable kink modes at high-pressure. For pressures greater than the ideal magnetohydrodynamic (MHD) stability limit, as calculated without a stabilizing wall, the 3D structure transitions in a way that is qualitatively predicted by an extended MHD model that includes kinetic wave-particle interactions. These changes in poloidal mode structure are correlated with the proximity of rotation profiles to thermal ion bounce and the precession drift frequencies suggesting that these kinetic resonances are modifying the relative amplitudes of the stable modes. These results imply that each kink may eventually be independently controlled.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

ENERGY DISSIPATION PROCESSES IN SOLAR WIND TURBULENCE

Turbulence is a chaotic flow regime filled by irregular flows. The dissipation of turbulence is a fundamental problem in the realm of physics. Theoretically, dissipation ultimately cannot be achieved without collisions, and so how turbulent kinetic energy is dissipated in the nearly collisionless solar wind is a challenging problem. Wave particle interactions and magnetic reconnection (MR) are two possible dissipation mechanisms, but which mechanism dominates is still a controversial topic. Here we analyze the dissipation region scaling around a solar wind MR region. We find that the MR region shows unique multifractal scaling in the dissipation range, while the ambient solar wind turbulence reveals a monofractal dissipation process for most of the time. These results provide the first observational evidences for intermittent multifractal dissipation region scaling around a MR site, and they also have significant implications for the fundamental energy dissipation process.

79 ASTRONOMY AND ASTROPHYSICS↗