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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Parallel Simulation of Beam Dynamics in Particle Accelerators [Slides]

Particle accelerators are among the most versatile and important tools of scientific discovery. The Nation's accelerators are responsible for a wealth of advances in materials science, chemistry, the biosciences, particle physics, and nuclear physics. They also have important applications to national security, the environment, energy, medicine, and on the quality of people's lives. LANL has a long history of making pioneering contributions to Accelerator Science including key contributions to the field of Computational Accelerator Physics. These include the development of early beam dynamics codes with space charge (such as PARMILA and PARMELA), the development of rf cavity codes and magnet codes (including Poisson and Superfish), and the development and distribution of codes to the accelerator community through the Los Alamos Accelerator Code Group. LANL researchers also helped pioneer the development of massively parallel space-charge codes. In project t22_accelsim we have moved beyond electrostatic models of collective effects (i.e., solving the Poisson equation in the bunch frame) to fully electromagnetic models based on the Lienard-Wiechert formalism. This approach enables the large-scale simulation of radiation production and collective effects in high brightness electron beams. This is highly relevant to LANL given its future goal of developing an X-ray Free Electron Laser (XFEL). It also directly impacts a LANL LDRD project to develop an undulator-based non-invasive beam profile monitor for beams created in laser-plasma accelerator systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Understanding the origin of softness in structurally tailored and engineered macromolecular (STEM) gels: A DPD study

Structurally tailored and engineered macromolecular (STEM) gels are polymer networks containing a primary network (the “STEM-0” gel) and side chains that are anchored to this network (forming the “STEM-1” gel). The ability to vary the features of the side chains provides a robust means of tailoring the macroscopic properties of the material. We use dissipative particle dynamics (DPD) simulations to determine the mechanical response of STEM-1 gels to uniaxial compression for various values of the side chain length, n sc . While the Young’s modulus of the material is significantly decreased with increasing n sc (at a fixed density of grafted side chains), above a certain saturation value, increasing n sc does not lead to any further softening of the sample. Using the simulations, we calculate the relevant stresses, which are related to the Young’s modulus and the number of entanglements in the network. We show that the backbone chains become more spread out upon addition of sidechains, leading to a decrease in the physical entanglements between backbone segments. This observation accounts for the decrease of stress in the backbone network and the softening of the networks through the addition of sidechains. For long chains, however, the number density of physical crosslinks between side chains is independent of side chain length; this observation explains the observed stress saturation in the STEM-1 gels. Our approach allows us to correlate the molecular architecture of the gels to the resultant macroscopic mechanical behavior and provide guidelines for fabricating STEM gels with well-defined mechanical properties, which allow the materials to be used for a variety of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Parallel Simulation of Beam Dynamics in Particle Accelerators [Slides]

LW3D was used to explore the expected CSR from an undulator-based diagnostic using a first-principles approach. It was also used for comparison with an analytic Green-function approach. LW3D was extended to model transport systems involving dipoles and quadrupoles. It was used to explore the radiation produced in a 4-dipole bunch compressor. Radiation ahead of the electron bunch at the beamline exit was shown to be associated with wavefronts produced in upstream dipoles overtaking the bunch.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Transport and losses of energetic particles in tokamaks in the presence of Alfvén activity using the new full orbit TAPaS code coupled to FAR3d

Recent developments and tools integrated into the TAPaS code are presented, enabling realistic scenario simulations of particle dynamics within experimental tokamak magnetic equilibria. In particular, the enhanced capabilities of TAPaS enable seamless coupling with external simulations, provided the metric and equilibrium magnetic field of the external code are known. Coupling TAPaS with the gyro-fluid code FAR3d, the transport and losses of energetic particles in the presence Alfvén eigenmodes (AEs) in DIII-D plasma discharge #159243 were investigated. Detailed analyses of prompt losses with and without collisions were performed. Then, further analysis was performed in the presence of electromagnetic perturbations resulting from AEs activity. The results indicate that, for the energies and the initial conditions considered here, the presence of AEs enhances the particle losses.

Alfven eigenmodes↗
DOE OSTI2024

Machine Learning of Performance Space Mapping for the DPD Simulation of Drug Delivery to Endothelial Cells

Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Thermodynamic stability versus kinetic accessibility: Pareto fronts for programmable self-assembly

A challenge in designing self-assembling building blocks is to ensure the target state is both thermodynamically stable and kinetically accessible. These two objectives are known to be typically in competition, but it is not known how to simultaneously optimize them. We consider this problem through the lens of multi-objective optimization theory: we develop a genetic algorithm to compute the Pareto fronts characterizing the tradeoff between equilibrium probability and folding rate, for a model system of small polymers of colloids with tunable short-ranged interaction energies. We use a coarse-grained model for the particles' dynamics that allows us to efficiently search over parameters, for systems small enough to be enumerated. For most target states there is a tradeoff when the number of types of particles is small, with medium-weak bonds favouring fast folding, and strong bonds favouring high equilibrium probability. Additionally, the tradeoff disappears when the number of particle types reaches a value m *, that is usually much less than the total number of particles. This general approach of computing Pareto fronts allows one to identify the minimum number of design parameters to avoid a thermodynamic–kinetic tradeoff. However, we argue, by contrasting our coarse-grained model's predictions with those of Brownian dynamics simulations, that particles with short-ranged isotropic interactions should generically have a tradeoff, and avoiding it in larger systems will require orientation-dependent interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Platinum nanoparticle compression: Combining in situ TEM and atomistic modeling

The mechanical behavior of nanoparticles governs their performance and stability in many applications. However, the small sizes of technologically relevant nanoparticles, with diameters in the range of 10 nm or less, significantly complicate experimental examination. These small nanoparticles are difficult to manipulate onto commercial test platforms and deform at loads that are below the typical noise floor of the testing instruments. Here, we synthesized small platinum nanoparticles directly onto a mechanical tester and used a modified nanomanipulator to enhance load resolution to the nanonewton scale. We demonstrated the in situ compression of an 11.5-nm platinum nanoparticle with simultaneous high-resolution measurements of load and particle morphology. Molecular dynamics simulations were performed on similarly sized particles to achieve complementary measurements of load and morphology, along with atomic resolution of dislocations. The experimental and simulation results revealed comparable values for the critical resolved shear stress for failure, 1.28 and 1.15 GPa, respectively. Altogether, this investigation demonstrated the promise of, and some initial results from, the combination of atomistic simulations and in situ experiments with an unprecedented combination of high spatial resolution and high load resolution to understand the behavior of metal nanoparticles under compression.

42 ENGINEERING↗
DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗
DOE OSTI2026

Kinetic Pathways to Diblock Copolymer Micelles via Thin Film Dissolution and Cosolvent Variation

We investigate how processing pathways control micelle formation during dissolution of lamellar diblock copolymers using dissipative particle dynamics (DPD) simulations and DPD-selfconsistent field theory (DPD-SCFT). Comparing thin film (TF) and cosolvent-assisted (CS) dissolution reveals fundamentally different micellization mechanisms and final states. In the TF pathway, dissolution proceeds through a metastable cylindrical intermediate that dictates final micelle dimensions. The lamellar melt first transforms into cylindrical micelles, which then fragment into spherical micelles. The final spherical radius is intrinsically linked to the cylinder radius by a constant ratio, Rsphere ≈ 1.4Rcylinder. DPD-SCFT confirms that both cylinders and spheres are near their equilibrium dimensions. In contrast, CS dissolution produces kinetically trapped micelles with sizes significantly below equilibrium. Micellization is hindered by high free energy barriers to single-chain exchange and fusion. Growth occurs primarily via fusion, but increasing corona repulsion suppresses further coarsening, preventing equilibration. These results demonstrate that, when starting from a bulk copolymer phase, the metastable cylindrical intermediate plays a central role in determining final micelle size.

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

Using kernel-based statistical distance to study the dynamics of charged particle beams in particle-based simulation codes

Measures of discrepancy between probability distributions (statistical distance) are widely used in the fields of artificial intelligence and machine learning. We describe how certain measures of statistical distance can be implemented as numerical diagnostics for simulations involving charged-particle beams. Related measures of statistical dependence are also described. The resulting diagnostics provide sensitive measures of dynamical processes important for beams in nonlinear or high-intensity systems, which are otherwise difficult to characterize. Here, the focus is on kernel-based methods such as maximum mean discrepancy, which have a well-developed mathematical foundation and reasonable computational complexity. Several benchmark problems and examples involving intense beams are discussed. While the focus is on charged-particle beams, these methods may also be applied to other many-body systems such as plasmas or gravitational systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Improved Precision Scaling for Simulating Coupled Quantum-Classical Dynamics

In this paper, we present a superpolynomial improvement in the precision scaling of quantum simulations for coupled quantum-classical systems. Such systems are found in, e.g., molecular-dynamics simulations within the Born-Oppenheimer approximation. By employing a framework based on the Koopman–von Neumann formulation of classical mechanics, we express the Liouville equation of motion as unitary dynamics and utilize phase kickback from a dynamical quantum simulation to calculate the quantum forces acting on classical particles. This approach allows us to simulate the dynamics of these classical particles without the overheads associated with measuring gradients and solving the equations of motion on a classical computer, resulting in a superpolynomial advantage at the price of increased space complexity. We demonstrate that these simulations can be performed in both microcanonical and canonical ensembles, enabling the estimation of thermodynamic properties from the prepared probability density. Published by the American Physical Society 2024

Simon, Sophia (ORCID:0000000332612338)↗
DOE OSTI2025

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.

aerosol chemistry dynamics↗
DOE OSTI2025

Self-Assembly of Anisotropic Particles on Curved Surfaces

The surface curvature of membranes, interfaces, and substrates plays a crucial role in shaping the self-assembly of particles adsorbed on these surfaces. However, little is known about the interplay between particle anisotropy and surface curvature and how they couple to alter the free energy landscape of particle assemblies. Using molecular dynamics simulations, we investigate the effect of prescribed curvatures on a quasi-2D assembly of anisotropic patchy particles. By varying curvature and surface coverage, we uncover a rich geometric phase diagram, with curvature inducing ordered structures entirely absent on planar surfaces. Large spatial domains of ordered structures can contain hidden microdomains of orientational textures imprinted by the surface on the assembly. The dynamical landscape is also reshaped by surface curvature, with a glass-like state emerging at modest densities and high curvature. Changes to the symmetry of the surface curvature are found to result in distinct structures, including phases with mesoscale ordering. In conclusion, our findings show that the coupling between surface curvature and particle geometry opens an unexplored space of morphologies and structures that can be exploited for material design.

Fluids↗
DOE OSTI2023

Stokesian dynamics with odd viscosity

Stokesian dynamics is a well-established computational method for simulating dynamics of many particles suspended in a conventional passive fluid medium. Active fluids composed of self-driving particles with broken time-reversal symmetry permit the emergence of a so-called odd viscosity. Here, in this work, we extended the conventional Stokesian dynamics formalism to incorporate the additional hydrodynamic effects due to odd viscosity, which enables simulating collective behavior of many particles suspended in an active fluid medium with both even viscosity and odd viscosity.

42 ENGINEERING↗