Search NASA⌕ Search

SEARCH · Search NASA

Results for “Particle dynamics”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Towards an understanding of particle-scale flaws and microstructure evolution in cold spray via accumulation of single particle impacts

Cold spray coatings are the sum of countless individual bonding events between single particles impacting on top of one another at high velocities. Thus, the collective behavior of microparticles must be considered to elucidate the origins of coating flaws at the scale of the particles and larger, or the dynamic evolution of the overall coating microstructure. Laser-induced particle impact testing (LIPIT) has been extensively used to study single-particle impacts, and in this work is adapted to study the accumulation of numerous particles with knowledge of each individual particle's impact parameters (particle size, velocity). The method reproducibly deposits stacks of gold particles (>20 particles) with different characteristic spectra of impact velocity. The observation of impact-induced erosion lets us define a critical velocity for material-build-up that is higher than that for single-particle bonding. The quantitative single-particle data are analyzed in a correlative manner to the structure and flaws in the resulting stacks, providing some first statistical connections between, e.g., strain and recrystallization, or aberrant particle characteristics and defects. The results highlight opportunities for the study of many-particle phenomena in microparticle impact—from interaction of particles in cold spray to multi-step erosion processes—with a quantitative view of the behavior of single particles.

Erosion↗
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

Probing the shear-induced microstructure of a smectite clay aqueous suspensions using rheo-USANS and rheo-SIPLI measurements

Hypothesis: The static microstructure of aqueous sodium-montmorillonite (Na-Mt) suspensions at low ionic strengths (where Particle Size/Debye Length ≈1) exhibits both the particle–particle ordering as well as aggregation with repulsive ordered domains having characteristic optical birefringence and attractive aggregated entities larger than 20 µm resulting in ever-increasing yield stresses also known as physical aging-rejuvenation behavior. We hypothesize that the attractive particle–particle aggregation is the underlying cause behind the physical aging-rejuvenation behavior observed in Na-Mt suspensions with no contribution from structural dynamics driven by repulsive particle–particle ordering or jamming. Experiments: We investigate the shear-induced microstructure of aqueous Na-Mt suspensions in the sol and gel state using rheo-ultra-small angle neutron scattering (rheo-USANS) experiments at shear rates of 1, 50, 500, and 2000 s −1 . We also perform rheo-shear-induced polarization light imaging (rheo-SIPLI) experiments to relate ordering with shearing and aging. Findings: Shearing the suspensions at low to moderate shear rates induces particle–particle aggregation and shearing at high shear rates induces the breakage of particle–particle aggregation in the sol and gel states, suggesting the microstructural aggregation in the sol and gel state is shear sensitive and a full rejuvenation or breakage of particle–particle aggregation is only achieved at a minimum critical shear rate. The rheo-SIPLI experiments reveal that the sol and gel state exhibited strong Maltese cross patterns at a shear rate of 1000 s −1 , indicating particle–particle ordering. Post shearing, the gel exhibited temporal evolution of storage modulus without any noticeable influence on the appearance of the Maltese cross pattern indicating physical aging and particle ordering are distinct length scale phenomena in Na-Mt suspensions and the physical aging-rejuvenation behaviour is a feature of particle–particle aggregation as opposed to ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Detecting shearless phase-space transport barriers in global gyrokinetic turbulence simulations with test particle map models

In magnetically confined fusion plasmas, the role played by zonal 𝐸 x 𝐵 flow shear layers in the suppression of turbulent transport is relatively well understood. However, less is understood about the role played by the weak shear regions that arise in the non-monotonic radial electric field profiles often associated with these shear layers. In electrostatic simulations from the global total-𝑓 gyrokinetic particle-in-cell code XGC, we demonstrate how shearless regions with non-zero flow curvature form zonal ‘jets’ that, in conjunction with neighbouring regions of shear, can act as robust barriers to particle transport and turbulence spreading. By isolating quasi-coherent fluctuations radially localised to the zonal jets, we construct a map model for the Lagrangian dynamics of gyrokinetic test particles in the presence of drift waves. We identify the presence of shearless invariant tori in this model and verify that these tori act as partial phase-space transport barriers in the simulations. We also demonstrate how avalanches impinging on these shearless tori cause eddy detachment events that form ‘cold/warm core ring’ structures analogous to those found in oceanic jets, facilitating transport across the barriers without destroying them completely. We discuss how shearless tori may generically arise from tertiary instabilities or other types of discrete eigenmodes, suggesting their potential relevance to broader classes of turbulent fluctuations.

fusion plasma↗
DOE OSTI2025

Glassy interphases reinforce elastomeric nanocomposites by enhancing percolation-driven volume expansion under strain

For nearly a century, introduction of nanoparticles to elastomers has yielded extraordinarily tough nanocomposites that are critical to technologies from actuators to tires. The mechanisms by which this reinforcement occurs have nevertheless remained a central open question in material science. One widely debated hypothesis posits that strong interactions between polymer and particles induce "glassy bridges" that cement particles into a cohesive percolating network that resists elongation. Here, molecular dynamics simulations show that glassy particle shells do not primarily provide elongational cohesion. Instead, they amplify an underlying mechanism wherein competition between filler and elastomer networks causes the elastomer's volume to increase on deformation. This induces contributions from the elastomer's bulk modulus, which is of order 1000 times larger than its Young's modulus. These findings establish a unified understanding of low-strain reinforcement in filled elastomers as emanating from volumetric competition between coexisting particulate and elastomeric networks. This reframes and unifies our understanding of low-strain reinforcement, provides a clear-cut diagnostic for the presence of glassy bridging, and offers a new design principle for tough elastomeric nanocomposites.

Computational Physics (physics.comp-ph)↗
DOE OSTI2025

Performance of wave function and Green's function methods for non-equilibrium many-body dynamics

Theoretical descriptions of the non-equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on the truncation of the expansion of the many-body wave function, (ii) compressed representations of the many-body wave function, or (iii) evolution of an effective (downfolded) representation through Green's functions. In this work, we select representative cases of each of the methods and address how these complementary approaches capture the dynamics driven by intense field perturbations to non-equilibrium states. Under strong driving, the systems are characterized by strong entanglement of the single-particle density matrix and natural populations approaching those of a strongly interacting equilibrium system. We generate a representative set of results that are numerically exact and form a basis for a critical comparison of the distinct families of methods. We demonstrate that the compressed formulation based on similarity-transformed Hamiltonians (coupled-cluster approach) is practically exact in weak fields and, hence, weakly or moderately correlated systems. Coupled cluster, however, struggles for strong driving fields, under which the system exhibits strongly correlated behavior, as measured by the von Neumann entropy of the single-particle density matrix. The dynamics predicted by Green's functions in the (widely popular) G W approximation are less accurate, but improve significantly upon the mean-field results in the strongly driven regime. Published by the American Physical Society 2025

Reeves, Cian C. (ORCID:0009000642581845)↗
DOE OSTI2025

Modeling the Nucleation and Growth of Lead Sulfate Particles on Lead Electrodes

Lead-acid batteries (LABs) play a pivotal role in the energy storage sector with applications spanning from starting-lighting-ignition batteries to grid energy storage. Passivation of lead negative electrodes by PbSO 4 particles is a fundamental mechanism limiting the performance of LAB. In this regard, an electrochemical model is developed that simulates the nucleation and growth (N&G) dynamics of PbSO 4 particles on a flat lead electrode, responsible for its passivation. The model considers the electrochemical reactions between lead electrode and sulfuric acid, N&G of PbSO 4 particles, passivation of the lead surface, and the ternary transport of PbSO 4 (aq), bisulfate, and protons in H 2 SO 4 electrolyte. The model is validated with a dataset of cyclic voltammetry (CV) responses collected at several scan rates and H 2 SO 4 concentrations. The model shows remarkable qualitative and quantitative agreement with the experimental data including CV peak features, discharge capacity, and particle size. The model was employed to explore key N&G quantities, such as supersaturation, nucleation rate, particle count, growth rate, particle size, and surface coverage, and to examine how their interactions influence electrode utilization. Parametric studies were also conducted to evaluate how scan rates and acid concentrations influence the previously mentioned N&G quantities and, subsequently, the utilization of the electrode.

25 ENERGY STORAGE↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2025

Development and characterization of an aerosol-generated multi-method analytical particle test material

Ceria (CeO 2 ) particles with low to ultra-low loading of nickel dopant were produced using an aerosol-based, droplet-to-particle synthesis via an in-line calcination technique. This aerosol-based synthesis method enables the production of particles with a monodisperse size distribution. These produced and well-characterized, multi-element, ceria-based particles demonstrate a material exemplar for multi-method analytical testing. They were prepared from a cerium nitrate feedstock where low loading nickel dopant was added at target Ni/(Ni + Ce) atomic percents of 1 %, 0.1 %, and 0.01 %, using a nickel nitrate spike. This methodology proved to produce ceria particles doped with a dynamic range of low to ultra-low loadings of nickel over a 24-h period, with consistent size distribution, morphology, and composition. The successful incorporation of nickel was demonstrated with bulk and single particle inductively coupled plasma mass spectroscopy and revealed notable particle-to-particle elemental homogeneity. X-ray photoelectron spectroscopy demonstrated the presence of a high concentration of nickel dopant incorporated preferentially toward the surface of the particles, and that this dopant aided oxidation of surface Ce(III) atoms to Ce(IV). These particle test materials were then validated through X-ray absorption near edge spectroscopy, comparing the ultra-low 0.01 % Ni and low 1 % Ni-doped ceria samples. This revealed a more-reduced oxidation state of the nickel with an increase in dopant concentration. Finally, this work demonstrates a synthesis and systematic characterization scheme to produce multi-method analytical test particulates.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

LeaPP: Learning Pathways to Polymorphs through Machine Learning Analysis of Atomic Trajectories

Understanding the mechanisms underlying crystal nucleation and growth is crucial for many technological applications. Due to the short length and time scales involved, crystal nucleation is often studied using molecular simulations. Most existing approaches to extract the nucleation mechanism from simulations focus on the analysis of static snapshots of the configurations, potentially overlooking subtle local fluctuations and the history of the particles involved in the formation of solid nuclei. Here, in this work, we propose a novel methodology called LeaPP that categorizes nucleation trajectories based on the temporal information of their constituent particles. We leverage the time evolution of the local environment of the crystallizing particles to encapsulate the relationship between the structure and dynamics and distinguish between different evolving particle paths. Identification of the distinct particle paths further enables characterizing the nucleation trajectories into different pathways. Collectively, LeaPP provides a more nuanced understanding of nucleation through an unsupervised approach with lesser dependence on traditional order parameters. Furthermore, the pathways identified by LeaPP are predictive of the resulting polymorph. We demonstrate LeaPP on three different systems─Lennard-Jones-like particles, Ni 3 Al, and water on surfaces. The general methodology underlying LeaPP─considering the time evolution of the building blocks─applies to a wide range of self-assembly problems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A review of low-rank methods for time-dependent kinetic simulations

Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six–dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics problems lives on a low dimensional manifold that can be described by a low-rank matrix or tensor approximation. We then review the recent development of so-called low-rank methods that evolve the solution on this manifold. The two classes of methods we review are the dynamical low-rank (DLR) method, which derives differential equations for the low-rank factors, and a Step-and-Truncate (SAT) approach, which projects the solution onto the low-rank representation after each time step. Thorough discussions of time integrators, tensor decompositions, and method properties such as structure preservation and computational efficiency are included. We further show examples of low-rank methods as applied to particle transport and plasma dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-accuracy method for modeling nucleation and growth of particles

State-of-the-art numerical models describing the kinetics of aerosol particle nucleation and growth from a cooling vapor primarily use a nodal method, in which particles that are smaller than the critical size are omitted from consideration because they are thermodynamically unfavorable. This omission is based on the assumption that most newly formed particles are above the critical size, so that subcritical-size particles are not important to take into account. Due to the nature of the nodal method, it suffers from numerical diffusion, which can cause an artificial broadening of the cluster size distribution leading to a significant overestimation of the number of large-size particles. To address these issues, we propose a more accurate numerical method that explicitly models particles of all sizes, and uses a special numerical scheme that substantially reduces the numerical diffusion and provides high solution accuracy and numerical stability. We extensively compare this novel method to the commonly used nodal solver of the general dynamic equation (GDE) for particle growth and demonstrate that it offers GDE solutions with higher accuracy with low numerical diffusion. Incorporating small subcritical clusters into the solution is crucial for: 1) more precise determination of the entire particle size distribution function and 2) wider applicability of the model to experimental studies with non-monotonic temperature variations leading to particle evaporation. The computational code implementing this numerical method in Python is available upon request.

42 ENGINEERING↗
DOE OSTI2025

Measurements and Analyses to Enable Science for the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE)

Coastal cities provide the opportunity to characterize the substantial effects of manmade particles on marine cloud properties and processes. La Jolla lies to the north of San Diego, California, but it is often about a day directly downwind of the major pollution sources located in the ports of Los Angeles and Long Beach. The large dynamic range of aerosol particle concentrations combined with the multi-hour to multi-day persistence of stratocumulus cloud layers makes the site ideal for investigating the seasonal changes in cloud and aerosol properties as well as the quantitative relationships between cloud and aerosol properties. The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) characterized the extent, radiative properties, aerosol interactions, and precipitation characteristics of stratocumulus clouds in the Eastern Pacific across all four seasons at two coastal sites in La Jolla. This project was designed to enhance and expand the scientific uses of the ARM AMF1 measurements from EPCAPE. The goal was to ensure meaningful observations were collected that would enable science. The project included the following objectives: (1) Reviewing the ARM AMF1 measurements [ARM, 2021a; b] and circulating a summary of the measurements each week, (2) Comparing AMF1 measurements to those provided by collaborators (including filter measurements at the pier), (3) Collecting and analyzing filter samples from Scripps Pier by Fourier Transform Infrared spectroscopy (FTIR) and X-ray Fluorescence (XRF), (4) Assisting in operations of instruments provided by Guest PIs when possible, and (5) Providing an initial compilation of EPCAPE aerosol and cloud seasonal differences. The expected outcomes of these objectives were enhanced proposals and publications using EPCAPE measurements by helping to identify instrument issues, expanded data access and awareness by distributing weekly plots and related summaries, improved source-related attribution of aerosols with elemental tracers, additional observations provided by Guest PIs, and accelerated ACI studies enabled by the compiled seasonal summaries of aerosol and cloud properties. Three examples of the science enabled by this project are findings that (i) aerosol and cloud aqueous production contributes more than half of sulfate particle mass concentration, (ii) upwind sources make chemical composition very similar at nearby sites despite local differences in meteorology, and (iii) most of the large mass concentration of semi-volatile organic components is co-emitted and co-evaporated with nitrate. Together these findings illustrate how ARM extended field campaigns in coastal regions can be used to constrain ACI processes with direct observations. By using the unique ARM suite of cloud radiative products in addition to the measured aerosol properties at a coastal location, we were able to address the more specific question of which aerosol particles cause how much of the effects on clouds. Identifying this signature in coastal areas provides an opportunity to test the representation of aerosol sources by global models in a range of clean and urban-influenced conditions.

Russell, Lynn [Univ. of California, San Diego, CA ↗
DOE OSTI2024

Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions

Aerosol effects on clouds and radiation are the dominant contribution to uncertainty in radiative forcing relative to the pre-industrial atmosphere. While previous studies have assessed the impact of parametric uncertainty on modeled forcing, structural errors from the numerical representation of particle distributions have not been well quantified. Here we present a framework for quantifying error in aerosol size distributions and cloud condensation nuclei activity, which we apply to the widely used 4-mode version of the Modal Aerosol Module (MAM4). Box model predictions from the MAM4 are evaluated against the Particle Monte Carlo Model for Simulating Aerosol Interactions and Chemistry (PartMC-MOSAIC), a benchmark model that tracks the evolution of individual particles. We show that size distributions simulated by MAM4 diverge from those simulated by PartMC-MOSAIC after only a few hours of aging by condensation and coagulation in polluted conditions, which leads to large errors in modeled cloud condensation nuclei concentrations. We find that differences between MAM4 and PartMC-MOSAIC are largest under polluted conditions, where the size distribution evolves rapidly though aging by condensation of semi-volatile substances and coagulation among particles. These findings suggest that structural error in modeled aerosol properties contributes to the large inter-model variability in aerosol radiative forcing.

Fierce, Laura M.↗
DOE OSTI2024

Axially symmetric e-lens based on McMillan map

In this work, we investigate the transverse dynamics of a single particle in a model integrable accelerator lattice, based on a McMillan axially symmetric electron lens. Although the McMillan e-lens has been considered as a device potentially capable of mitigating collective space charge forces, some of its fundamental properties have not been described yet. The main goal of our work is to close this gap and understand the limitations and potential of this device. We classify possible regimes with stable trajectories and provide set of canonical action-angle variables, along with an evaluation of the dynamical aperture, Poincar\'e rotation numbers as functions of amplitudes, and spread in nonlinear tunes.

43 PARTICLE ACCELERATORS↗