Dynamic-template-directed multiscale assembly for large-area coating of highly-aligned conjugated polymer thin films
Abstract not provided
Mohammadi, Erfan↗
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Multiscale model based design of an energy–intensified novel adsorptive reactor process for the water gas shift reaction
In this work, an adsorptive reactor (AR) process is considered that can energetically intensify the water gas shift reaction (WGSR). To best understand AR process behavior, a multiscale, dynamic, process model is developed. This multiscale model enables the quantification of catalyst and adsorbent effectiveness factors within the reactor environment, obliviating the commonly employed assumption that these factors are constant. Simulations of the AR's alternating adsorption-reaction/desorption operation, using the proposed model, illustrate rapid convergence to a long-term periodic solution. The obtained simulation results quantify the influence of key operating conditions and design parameters (e.g., reactor temperature/pressure, W cat /F CO , W ad /F CO , F H2O /F CO ratios, and pellet size) on the AR's behavior. Finally, they also demonstrate, for pellet diameters used at the industrial scale, significant temporal and axial variation of the catalyst/adsorbent pellet effectiveness factors. Finally, the energetic intensification benefits of the proposed AR process over conventional WGSR packed-bed reactors are quantified.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Operator learning for predicting multiscale bubble growth dynamics
We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Energy transfer and scale dynamics in 2D and 3D laser-driven jets
We demonstrate a methodology for diagnosing the multiscale dynamics and energy transfer in complex HED flows with realistic driving and boundary conditions. The approach separates incompressible, compressible, and baropycnal contributions to energy scale-transfer and quantifies the direction of these transfers in (generalized) wavenumber space. We use this to compare the kinetic energy (KE) transfer across scales in simulations of 2D axisymmetric vs fully 3D laser-driven plasma jets. Using the FLASH code, we model a turbulent jet ablated from an aluminum cone target in the configuration outlined. We show that, in addition to its well known bias for underestimating hydrodynamic instability growth, 2D modeling suffers from significant spurious energization of the bulk flow by a turbulent upscale cascade. In 2D, this arises as vorticity and strain from instabilities near the jet's leading edge transfer KE upscale, sustaining a coherent circulation that helps propel the axisymmetric jet farther ( ≈25% by 3.5 ns) and helps keep it collimated. In 3D, the coherent circulation and upscale KE transfer are absent. Here, the methodology presented here may also help with inter-model comparison and validation, including future modeling efforts to alleviate some of the 2D hydrodynamic artifacts highlighted in this study.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A seamless multiscale operator neural network for inferring bubble dynamics
Modelling multiscale systems from nanoscale to macroscale requires the use of atomistic and continuum methods and, correspondingly, different computer codes. Here, we develop a seamless method based on DeepONet, which is a composite deep neural network (a branch and a trunk network) for regressing operators. In particular, we consider bubble growth dynamics, and we model tiny bubbles of initial size from 100 nm to 10 $\mathrm {\mu }\textrm {m}$ , modelled by the Rayleigh–Plesset equation in the continuum regime above 1 $\mathrm {\mu }\textrm {m}$ and the dissipative particle dynamics method for bubbles below 1 $\mathrm {\mu }\textrm {m}$ in the atomistic regime. After an offline training based on data from both regimes, DeepONet can make accurate predictions of bubble growth on-the-fly (within a fraction of a second) across four orders of magnitude difference in spatial scales and two orders of magnitude in temporal scales. The framework of DeepONet is general and can be used for unifying physical models of different scales in diverse multiscale applications.
Mechanics↗
Multiscale Explanation of the Missing Gallium Vacancy in Gallium Arsenide
Irradiation of gallium arsenide (GaAs) produces immobile vacancies and mobile interstitials. Yet, after decades of experimental investigation, the immobile Ga vacancy continues to evade detection, raising the question: where is the Ga vacancy? Static first-principles calculations predict a Ga vacancy should be readily observed. We find that short-time dynamical evolution of primary defects is the key to explaining this conundrum. Using a dynamical multiscale atomistically informed device engineering (AIDE) method, we discover that during the initial displacement damage, the Ga vacancy (3-/2-) defect level pins the Fermi level near the midgap, producing oppositely charged vacancies and interstitials. Driven by Coulomb attraction, fast As interstitials preferentially annihilate Ga vacancies. The Ga vacancy population plummets below detectable limits—and the now unpinned Fermi level recovers—before being experimentally observed. This dynamical model solves the mystery of the missing Ga vacancy and reveals the importance of a multiscale approach to explore the dynamical chemical behavior in experimentally inaccessible short-time regimes.
Diaz, Leopoldo [Sandia National Laboratories (SNL-↗
Prediction of Thermal Transport Properties of Materials with Microstructural Complexity
This project aims at overcoming the major obstacle standing in the way of progress in dynamic multiscale simulation, which is the lack of a concurrent atomistic-continuum method that allows phonons, heat and defects to pass through the atomistic-continuum interface. The research has led to the development of a concurrent atomistic-continuum (CAC) methodology for multiscale simulations of materials microstructural, mechanical and thermal transport behavior. Its efficacy has been tested and demonstrated through simulations of dislocation dynamics and phonon transport coupled with microstructural evolution in a variety of materials and through providing visual evidences of the nature of phonon transport, such as showing the propagation of heat pulses in single and polycrystalline solids is partially ballistic and partially diffusive. In addition to providing understanding on phonon scattering with phase interface and with grain boundaries, the research has contributed a multiscale simulation tool for understanding of the behavior of complex materials and has demonstrated the capability of the tool in simulating the dynamic, in situ experimental studies of nonequilibrium transient transport processes in material samples that are at length scales typically inaccessible by atomistically resolved methods.
36 MATERIALS SCIENCE↗
Cluster Morphology-Polymer Dynamics Correlations in Sulfonated Polystyrene Melts: Computational Study
Reaching exceptionally long times up to 500 ns in equilibrium and nonequilibrium molecular dynamics simulations studies, we have attained a fundamental molecular understanding of the correlation of ionomer clusters structure and multiscale dynamics, providing new insight into one critical, long-standing challenge in ionic polymer physics. The cluster structure in melts of sulfonated polystyrene with Na + and Mg 2+ counterions are resolved and correlated with the dynamics on multiple length and time scales extracted from measurements of the dynamic structure factor and shear rheology. We find that as the morphology of the ionic clusters changes from ladderlike for Na + to disordered structures for Mg 2+ , the dynamic structure factor is affected on the length scale corresponding to the ionic clusters. Lastly, rheology studies show that the viscosity for Mg 2+ melts is higher than for Na + ones for all shear rates, which is well correlated with the larger ionic clusters’ size for the Mg 2+ melts.
74 ATOMIC AND MOLECULAR PHYSICS↗
Multiphase computational fluid dynamics modeling of reacting flows in absorption columns for carbon capture
First-principles derived computational fluid dynamics (CFD) simulations have been proposed as a fundamental tool for investigating solvent-based CO 2 absorption in packed columns due to their ability to accurately represent the underlying nonlinear, multiscale dynamics. Numerous studies have previously utilized such CFD simulations to investigate hydrodynamics of columns with structured and random packings by assessing the key hydrodynamic metrics such as the interfacial and wetted areas. While mapping such metrics for different conditions is essential to the optimization of absorption columns, it is not sufficient, as the CO 2 capture rate depends also on the coupled, nonlinear dynamics from the underlying chemical reaction kinetics, thermodynamics, and heat-transfer rates. In this work, we present detailed CFD simulation results augmented by incorporating the effects of interfacial physical mass transfer of CO 2 , heat release from chemical reaction kinetics, and thermophysical property variations from resulting temperature gradients. We demonstrate the applicability of the proposed approach in numerically assessing the performance of packed columns by evaluating key hydrodynamic quantities, CO 2 absorption rates, and temperature rise in a reference column with packings that are structurally similar to the Sulzer Mellapak™ 250.Y packing, for different solvent inflow velocities and temperatures. Predictions from simulation results are found to be consistent with the trends in experimental observations from the literature, suggesting that the predictive capabilities of the simulation framework can be leveraged to guide the future development of absorber-column designs and optimized process flowsheets.
Absorption columns↗
Validation Framework for Post-Combustion Carbon Capture CFD Simulations
First-principles based computational fluid dynamics (CFD) simulations are proposed as a fundamental tool for investigating solvent-based CO2 absorption in packed columns, due to the ability to accurately represent the underlying non-linear multiscale dynamics. In this work, we employ such models to investigate hydrodynamics of columns with structured by assessing the key hydrodynamic metrics, such as pressure drop and liquid holdup. Our models are validated with experimental data from a specifically designed column for this line of work. The test cases of gas and liquid flowrates and operating conditions were selected through a comprehensive sequential design of experiments approach offered by the CCSI2 toolset.
Panagakos, Grigorios↗
Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration
Electronic structure calculations in the time domain provide a deeper understanding of nonequilibrium dynamics in materials. The real-time Boltzmann equation (rt-BTE), used in conjunction with accurate interactions computed from first principles, has enabled reliable predictions of coupled electron and lattice dynamics. However, the timescales and system sizes accessible with this approach are still limited, with two main challenges being the different timescales of electron and phonon interactions and the cost of computing collision integrals. As a result, only a few examples of these calculations exist, mainly for two-dimensional (2D) materials. Here we leverage adaptive and multirate time integration methods to achieve a major step forward in solving the coupled rt-BTEs for electrons and phonons. Relative to conventional (non-adaptive) time-stepping, our approach achieves a 10x speedup for a target accuracy, or greater accuracy by 3–6 orders of magnitude for the same computational cost, enabling efficient calculations in both 2D and bulk materials. This efficiency is showcased by computing the coupled electron and lattice dynamics in graphene up to ~100 ps, as well as modeling ultrafast lattice dynamics and thermal diffuse scattering maps in bulk materials (silicon and gallium arsenide). In addition to improved efficiency, our adaptive method can resolve the characteristic rates of different physical processes, thus naturally bridging different timescales. This enables simulations of longer timescales and provides a framework for modeling multiscale dynamics of coupled degrees of freedom in matter. Our work opens new opportunities for quantitative studies of nonequilibrium physics in materials, including driven lattice dynamics with phonons coupled to electrons, spin, and other degrees of freedom.
Yao, Jia [California Institute of Technology (CalT↗
Magnetospheric Multiscale Mission Attitude Dynamics: Observations from Flight Data
The NASA Magnetospheric Multiscale mission, launched on Mar. 12, 2015, is flying four spinning spacecraft in highly elliptical orbits to study the magnetosphere of the Earth. Extensive attitude data is being collected, including spin rate, spin axis orientation, and nutation rate. The paper will discuss the various environmental disturbance torques that act on the spacecraft, and will describe the observed results of these torques. In addition, a slow decay in spin rate has been observed for all four spacecraft in the extended periods between maneuvers. It is shown that this despin is consistent with the effects of an additional disturbance mechanism, namely that produced by the Active Spacecraft Potential Control devices. Finally, attitude dynamics data is used to analyze a micrometeoroid/orbital debris impact event with MMS4 that occurred on Feb. 2, 2016.
Conjunction↗
A flux-balanced fluid model for collisional plasma edge turbulence: Numerical simulations with different aspect ratios
We investigate the drift wave–zonal flow dynamics in a shearless slab geometry with the new flux-balanced Hasegawa-Wakatani model. As in previous Hasegawa-Wakatani models, we observe a sharp transition from a turbulence dominated regime to a zonal jet dominated regime as we decrease the plasma resistivity. However, unlike previous models, zonal structures are always present in the flux-balanced model, even for high resistivity, and strongly reduce the level of particles and vorticity flux. The more robust zonal jets also have a higher variability than in previous models, which is further enhanced when the computational domain is chosen to be elongated in the radial direction. In these cases, we observe complex multiscale dynamics, with multiple jets interacting with one another, and intermittent bursts. We present a detailed statistical analysis which highlights how the changes in the aspect ratio of the computational domain affect the third-order statistical moments, and thus modify the turbulent dynamics. By changing the aspect ratio and extending either the radial or the binormal direction, the present model study offers a better approximation to mimic turbulence in flux tube simulations for either very low magnetic shear or very high magnetic shear systems.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Stochastic averaging and sensitivity analysis for two scale reaction networks
In the presence of multiscale dynamics in a reaction network, direct simulation methods become inefficient as they can only advance the system on the smallest scale. This work presents stochastic averaging techniques to accelerate computations for obtaining estimates of expected values and sensitivities with respect to the steady state distribution. A two-time-scale formulation is used to establish bounds on the bias induced by the averaging method. Further, this formulation provides a framework to create an accelerated “averaged” version of most single-scale sensitivity estimation methods. In particular, we propose the use of a centered ergodic likelihood ratio method for steady state estimation and show how one can adapt it to accelerated simulations of multiscale systems. Lastly, we develop an adaptive “batch-means” stopping rule for determining when to terminate the micro-equilibration process.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
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Rayleigh-Plesset equation↗
Mechanisms underlying the active self-assembly of microtubule rings and spools
Here, active self-assembly offers a powerful route for the creation of dynamic multiscale structures that are presently inaccessible with standard microfabrication techniques. One such system uses the translation of microtubule filaments by surface-tethered kinesin to actively assemble nanocomposites with bundle, ring, and spool morphologies. Attempts to observe mechanisms involved in this active assembly system have been hampered by experimental difficulties with performing observation during buffer exchange and photodamage from fluorescent excitation. In the present work, we used a custom microfluidic device to remove these limitations and directly study ring/spool formation, including the earliest events (nucleation) that drive subsequent nanocomposite assembly. Three distinct formation events were observed: pinning, collisions, and induced curvature. Of these three, collisions accounted for the majority of event leading to ring/spool formation, while the rate of pinning was shown to be dependent on the amount of photodamage in the system. We further showed that formation mechanism directly affects the diameter and rotation direction of the resultant rings and spools. Overall, the fundamental understanding described in this work provides a foundation by which the properties of motor-driven, actively assembled nanocomposites may be tailored toward specific applications.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Time-resolved tracking of cellulose biosynthesis and assembly during cell wall regeneration in live Arabidopsis protoplasts
Cellulose, the most abundant polysaccharide on earth composing plant cell walls, is synthesized by coordinated action of multiple enzymes in cellulose synthase complexes embedded within the plasma membrane. Multiple chains of cellulose fibrils form intertwined extracellular matrix networks. It remains largely unknown how newly synthesized cellulose is assembled into an intricate fibril network on cell surfaces. Here, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly onArabidopsis thalianaprotoplast surfaces as the primary cell wall regenerates. Our observations provide the basis for a model of cellulose fibril network development in protoplasts driven by an interplay of multiscale dynamics that includes rapid diffusion and coalescence of nascent cellulose fibrils, processive elongation of single fibrils, and cellulose fibrillar network rearrangement during maturation. This study provides fresh insights into the dynamic and mechanistic aspects of cell wall synthesis at the single-cell level.
Science & Technology - Other Topics↗
Global Evolution of Solar Magnetic Fields and Prediction of Solar Activity Cycles
Prediction of solar activity cycles is challenging because the physical processes inside the Sun involve a broad range of multiscale dynamics that no model can reproduce, and the available observations are highly limited and cover mostly surface layers. Helioseismology makes it possible to probe solar dynamics in the convective zone, but variations in the differential rotation and meridional circulation are currently available for only two solar activity cycles. It has been demonstrated that sunspot observations, which cover over 400 years, can be used to calibrate the Parker-Kleeorin-Ruzmaikin model and that the Ensemble Kalman Filter (EnKF) method can be used to link the model magnetic fields to sunspot observations to make reliable predictions of a following cycle. However, for more accurate predictions, it is necessary to use actual observations of the solar magnetic fields, which are available for only four solar cycles. This raises the question of how limitations in observational data and model uncertainties affect predictive capabilities and implies the need for the development of new forecast methodologies and validation criteria. In this presentation, I will discuss the influence of the limited number of available observations on the accuracy of EnKF estimates of solar cycle parameters.
Kitiashvili, Irina N.↗