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DOE OSTI2017

Mesoscale Effective Property Simulations Incorporating Conductive Binder

Lithium-ion battery electrodes are composed of active material particles, binder, and conductive additives that form an electrolyte-filled porous particle composite. The mesoscale (particle-scale) interplay of electrochemistry, mechanical deformation, and transport through this tortuous multi-component network dictates the performance of a battery at the cell-level. Effective electrode properties connect mesoscale phenomena with computationally feasible battery-scale simulations. We utilize published tomography data to reconstruct a large subsection (1000+ particles) of an NMC333 cathode into a computational mesh and extract electrode-scale effective properties from finite element continuum-scale simulations. We present a novel method to preferentially place a composite binder phase throughout the mesostructure, a necessary approach due difficulty distinguishing between non-active phases in tomographic data. We compare stress generation and effective thermal, electrical, and ionic conductivities across several binder placement approaches. Isotropic lithiation-dependent mechanical swelling of the NMC particles and the consideration of strain-dependent composite binder conductivity significantly impact the resulting effective property trends and stresses generated. Lastly, our results suggest that composite binder location significantly affects mesoscale behavior, indicating that a binder coating on active particles is not sufficient and that more accurate approaches should be used when calculating effective properties that will inform battery-scale models in this inherently multi-scale battery simulation challenge.

25 ENERGY STORAGE↗

DOE OSTI2021

A Simple Lagrangian Parcel Model for the Initiation of Summer-time Mesoscale Convective Systems over the Central United States

Mesoscale convective systems (MCSs) account for more than 50% of summertime precipitation over the central United States and have a significant impact on local weather and hydrologic cycle. It is hypothesized that the inadequate treatment of MCSs is responsible for the long-standing warm and dry bias over the central United States in coarse-resolution general circulation model (GCM) simulations. In particular, a better understanding of MCS initiation is still lacking. Here a single-column Lagrangian parcel model is first developed to simulate the basic features of a rising parcel. This simple model demonstrates the collective effects of boundary layer moistening and dynamical lifting in triggering convective initiation and reproduces successfully its early afternoon peak with surface equivalent potential temperature as a controlling factor. It also predicts that convection is harder to trigger in the future climate under global warming, consistent with the results from convection-permitting regional climate simulations. Then, a multicolumn model that includes an array of single-column models aligned in the east–west direction and incorporates idealized cold pool interaction mechanisms is developed. The multicolumn model captures readily the cold pool–induced upscale growth feature in MCS genesis from initially scattered convection that is organized into a mesoscale cluster in a few hours. It also highlights the crucial role of lifting effects due to cold pool collision and spreading, subsidence effect, and gust front propagation speed in controlling the final size of mesoscale clusters and cold pool regions. This simple model should be useful for understanding fundamental mechanisms of MCS initiation and providing guidance for improving MCS simulations in GCMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Toward Understanding the Differences between Mesoscale and Large-Eddy Simulations of Tropical Cyclones

In this work, we investigate the ability of mesoscale and large-eddy simulation (LES) model configurations to predict the mean wind speed profile within the boundary layer of tropical cyclones (TCs). To this end, we perform idealized simulations of five hypothetical intense storms ranging from categories 1 to 5 on the Saffir–Simpson scale and extract time-averaged quantities near the eyewall region. We compare the model-generated data against mean wind speed profiles compiled from dropsondes launched from reconnaissance aircraft operating in the North Atlantic basin. Our analysis shows that mesoscale- and LES-generated mean wind fields display important differences in the boundary layer, including the magnitude of shear as well as the height where their low-level wind speed maxima are located. In addition, a comparison between the two model configurations with the dropsonde data shows that both modeling approaches are unable to capture the typical structure of mean winds in the lower part of the TC boundary layer (10–500 m), calling into question the use of simulations of near-axisymmetric storms for investigating the wind structure of past events. To better understand these differences, we conduct a momentum-budget analysis and show that modeled turbulent fluxes are underestimated in the mesoscale boundary layer parameterization compared to the LES model. Based on the analysis of the horizontal turbulent fluxes and their potential impact on mean flow quantities, a TC-specific boundary layer parameterization may be needed.

17 WIND ENERGY↗

DOE OSTI2023

Impact of Global Warming on U.S. Summertime Mesoscale Convective Systems: A Simple Lagrangian Parcel Model Perspective

Abstract Mesoscale convective systems (MCSs) are the dominant rainfall producer over the United States during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here, a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central United States and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean-state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust-front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multicolumn parcel model employing those parameterizations captures readily the cold pool–induced upscale growth feature. It simulates smaller mesoscale clusters over the central United States under global warming due to gust-front slowdown and subsidence strength enhancement, which are further attributed to land aridity–induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection lifetime lengthening, and cold pool mechanical-lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Mesoscale Connections Spring 2019

Mesoscale Connections is published by the Los Alamos National Laboratory's Physical Sciences Directorate. Its goal is to promote awareness of mesoscale materials research relevant to the NNSA, advances in mesoscale science capabilities at user facilities, and modeling challenges and needs for big data sets in service of materials co-design.

36 MATERIALS SCIENCE↗

NASA NTRS2018

Three-Dimensional, Space-Dependent Mesoscale Diffusivity: Derivation and Implications

Recently, we presented a parameterization of an arbitrary tracer 3D mesoscale flux that describes both diabatic and adiabatic regimes without using arbitrary tapering functions. However, we did not parameterize the mesoscale diffusivity, which is the subject of this work. A key difference between the present and previous diffusivity parameterizations is that in the latter, the two main ingredients, mesoscale drift velocity and eddy kinetic energy, were not parameterized but determined using present data, which deprives the models of predictive power. Since winds, stratification, etc., are predicted to change in the future, use of these parameterizations to study future climate scenarios becomes questionable. In this work, we parameterize drift velocity and eddy kinetic energy (vertical–horizontal components), which we first assess with data [WOCE, TOPEX/Poseidon (T/P), and North Atlantic Tracer Release Experiment (NATRE)] and then use in a coarse-resolution stand-alone ocean code under Coordinated Ocean-Ice Reference Experiment I (CORE-I) forcing. We present results for the global ocean temperature and salinity, Atlantic overturning circulation, meridional heat transport, and Drake Passage transport, which we compare with several previous studies. The temperature drift is less than that of five of seven previous OGCMs, and the salinity drift is among the smallest in those studies. The predicted winter Antarctic Circumpolar Current mixed layer depths (MLDs) are in good agreement with the data. Predicting the correct MLD is important in climate studies since models that predict very deep mixed layers transfer more of the radiative perturbation to the deep ocean, reducing surface warming (and vice versa).

Eddies; Mesoscale processes; Mixing; Ocean models;↗

DOE OSTI2014

Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)

Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

17 WIND ENERGY↗

DOE OSTI2026

Directing Assembly of Mesoscale Multi‐Shell Morphologies of DNA Origami Crystals

Nature builds hierarchically ordered materials, such as seashells, wood, and bones, through spatially and temporally regulated growth. Mimicking such a level of control in synthetic systems remains challenging, particularly in achieving multiscale organizations with prescribed nanoscale arrangements and desired material morphologies. In this study, we introduce a DNA-based self-assembly strategy for constructing diverse multi-shell mesoscale morphologies from nanoscale lattices, enabling prescribed structural, and compositional 3D material patterns. Using DNA origami frames as modular monomers, we direct anisotropic epitaxial growth through addressable DNA frame binding motifs and encapsulate nanoparticles (NPs) in desired 3D patterns. Sequential monomer addition under thermodynamically favorable conditions enables shell growth through heterogeneous nucleation while minimizing unwanted homogeneous nucleation. Here, we demonstrate that DNA-encoded addressability enables epitaxial shell growth along specific lattice directions, yielding crystals with multilayered mesoscale organization, including tube-like (sushi roll) and plate-like (macaron) morphologies. Shell-specific NP configurations and compositions are achieved through addressable and differentiated placement of NPs within each shell, as validated by small-angle x-ray scattering and cross-sectional scanning transmission electron microscopy. We further demonstrate addressable NP release and reveal that shells modulate release kinetics. Together, these findings establish a platform for fabricating DNA origami crystals with programmable mesoscale morphologies, nanoscale structure, composition, and transport properties.

3D patterning↗

DOE OSTI2017

A review of slip transfer: applications of mesoscale techniques

In this review article, we present and discuss recent mesoscale modeling studies of slip transmission of dislocations through biphase interfaces. Specific focus is given to fcc/fcc material systems. We first briefly review experimental, atomistic, and continuum-scale work that has helped to shape our understanding of these systems. Then several mesoscale methods are discussed, including Peierls–Nabarro models, discrete dislocation dynamics models, and phase field-based techniques. Recent extensions to the mesoscale mechanics technique called phase field dislocation dynamics are reviewed in detail. Results are compiled and discussed in terms of the proposed guidelines that relate composite properties to the critical stress required for a slip transmission event.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles

Macro-scale computations of shocked particulate flows require closure laws that model the exchange of momentum/energy between the fluid and particle phases. Closure laws are constructed in this work in the form of surrogate models derived from highly resolved mesoscale computations of shock-particle interactions. The mesoscale computations are performed to calculate the drag force on a cluster of particles for different values of Mach Number and particle volume fraction. Two Kriging-based methods, viz. the Dynamic Kriging Method (DKG) and the Modified Bayesian Kriging Method (MBKG) are evaluated for their ability to construct surrogate models with sparse data; i.e. using the least number of mesoscale simulations. It is shown that if the input data is noise-free, the DKG method converges monotonically; convergence is less robust in the presence of noise. The MBKG method converges monotonically even with noisy input data and is therefore more suitable for surrogate model construction from numerical experiments. This work is the first step towards a full multiscale modeling of interaction of shocked particle laden flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗

DOE OSTI2011

Mesoscale morphologies in polymer thin films.

In the midst of an exciting era of polymer nanoscience, where the development of materials and understanding of properties at the nanoscale remain a major R&D endeavor, there are several exciting phenomena that have been reported at the mesoscale (approximately an order of magnitude larger than the nanoscale). In this review article, we focus on mesoscale morphologies in polymer thin films from the viewpoint of origination of structure formation, structure development and the interaction forces that govern these morphologies. Mesoscale morphologies, including dendrites, holes, spherulites, fractals and honeycomb structures have been observed in thin films of homopolymer, copolymer, blends and composites. Following a largely phenomenological level of description, we review the kinetic and thermodynamic aspects of mesostructure formation outlining some of the key mechanisms at play. We also discuss various strategies to direct, limit, or inhibit the appearance of mesostructures in polymer thin films as well as an outlook toward potential areas of growth in this field of research.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Visualizing Current Flow at the Mesoscale in Disordered Assemblies of Touching Semiconductor Nanocrystals

The transport of electrons through assemblies of nanocrystals is important to performance in optoelectronic applications for these materials. Previous work has primarily focused on single nanocrystals or transitions between pairs of nanocrystals. There is a gap in knowledge of how large numbers of nanocrystals in an assembly behave collectively, and how this collective behavior manifests at the mesoscale. In this work, the variable range hopping (VRH) transport of electrons in disordered assemblies of touching, heavily doped ZnO nanocrystals was visualized at the mesoscale as a function of temperature both theoretically, using the model of Skinner, Chen and Shklovskii (SCS), and experimentally, with conductive atomic force microscopy on ultrathin films only a few particle layers thick. Agreement was obtained between the model and experiments, with a few notable exceptions. The SCS model predicts that a single network within the nanocrystal assembly, comprised of sites connected by small resistances, dominates conduction - namely the optimum band from variable range hopping theory. However, our experiments revealed that in addition to the optimum band, there are subnetworks that appear as additional peaks in the resistance histogram of conductive atomic force microscopy (CAFM) maps. Furthermore, the connections of these subnetworks to the optimum band change in time, such that some subnetworks become connected to the optimum band while others become disconnected and isolated from the optimum band; this observation appears to be an experimental manifestation of the ‘blinking’ phenomenon in our images of mesoscale transport.

Chen, Qinyi↗

DOE OSTI2024

Revealing Mesoscale Ionomer Membrane Structure by Tender Resonant X-ray Scattering

Nafion, a perfluorosulfonic acid ionomer, has been well-studied for decades due to its key role as an ion-conductive membrane in electrochemical energy conversion and storage applications. When hydrated, this membrane phase separates into a complex hierarchical nanostructure with hydrophilic domains that facilitate ion transport. Hard X-ray scattering has been a powerful technique in understanding Nafion due to its capabilities in capturing the ionomer’s nanophase separated structure, which gives rise to contrast between polymer and water domains. More recently, resonant X-ray scattering, which tunes to elemental absorption edges to provide specificity on constituent elements, has been explored to highlight key interactions related to the sulfonic acid groups within its structure. Here, in this work, we study the Nafion nanostructure by combining hard X-ray scattering and tender resonant X-ray scattering (TReXS) at the sulfur K-edge to reveal a mesoscopic feature corresponding to a correlation length of approximately 40 nm that has been challenging to resolve with hard X-ray studies. Additionally, we study the effect of the dispersion solvent composition that plays a key role in the formation of this mesoscale feature. Notably, TReXS can attain high contrast to decipher this mesoscale morphology even for dry polymer membranes under a vacuum, which typically have reduced contrast for hard X-rays. We find that the correlation length of this mesoscale feature decreases with increasing water fraction in the dispersion, which is the opposite trend exhibited by the smaller intercrystalline feature in the same membranes. This study showcases the utility of TReXS to uncover multiscale morphological details in functional polymers that are not always revealed by other methods like hard X-ray scattering. We illustrate this with Nafion, which is a relevant ion-conducting polymer for electrochemical technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Better Catalysts through Microscopy: Mesoscale M1/M2 Intergrowth in Molybdenum–Vanadium Based Complex Oxide Catalysts for Propane Ammoxidation

Catalysis research has transformed from the predominantly empirical field to one where it is possible to control the catalytic properties via characterization and modification of the atomic-scale active centers. Many phenomena in catalysis, such as synergistic effect, however, transcend the atomic scale and also require the knowledge and control of the mesoscale structure of the specimen to harness. Our paper, we use our discovery of atomic-scale epitaxial interfaces in molybdenum vanadium based complex oxide catalysts systems (i.e., MoVMO, M = Ta, Te, Sb, Nb, etc.) to achieve control of the mesoscale structure of this complex mixture of very different active phases. We can now achieve true epitaxial intergrowth between the catalytically critical M1 and M2 phases in the system that are hypothesized to have synergistic interactions, and demonstrate that the resulting catalyst has improved selectivity in the initial studies. Finally, we highlight the crucial role atomic scale characterization and mesoscale structure control play in uncovering the complex underpinnings of the synergistic effect in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Sensitivity Analysis of Wind and Turbulence Predictions With Mesoscale‐Coupled Large Eddy Simulations Using Ensemble Machine Learning

Abstract Coupling between mesoscale models and large‐eddy simulation (LES) models is increasingly used to more realistically represent the wide range of scales of atmospheric motions affecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale‐to‐microscale coupled modeling frameworks are potentially affected by a large number of uncertain closure parameters. Here, we investigate the sensitivity associated with six closure parameters related to a 1.5‐order subgrid‐scale turbulence closure for an ensemble of mesoscale‐coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project. Machine learning algorithms are used to explore the sensitivity of LES predictions, considering the effects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coefficient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for several of the simulation response variables we examine.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Colossal magnetic phase transition asymmetry in mesoscale FeRh stripes

Coupled order parameters in phase-transition materials can be controlled using various driving forces such as temperature, magnetic and electric field, strain, spin-polarized currents and optical pulses. Tuning the material properties to achieve efficient transitions would enable fast and low-power electronic devices. Here we show that the first-order metamagnetic phase transition in FeRh films becomes strongly asymmetric in mesoscale structures. In patterned FeRh stripes we observed pronounced supercooling and an avalanche-like abrupt transition from the ferromagnetic to the antiferromagnetic phase, while the reverse transition remains nearly continuous over a broad temperature range. Although modest asymmetry signatures have been found in FeRh films, the effect is dramatically enhanced at the mesoscale. The activation volume of the antiferromagnetic phase is more than two orders of magnitude larger than typical magnetic heterogeneities observed in films. Finally, the collective behaviour upon cooling results from the role of long-range ferromagnetic exchange correlations that become important at the mesoscale and should be a general property of first-order metamagnetic phase transitions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Soil moisture gradients strengthen mesoscale convective systems by increasing wind shear

Mesoscale convective systems are a class of storm linked to extensive flooding and other destructive hazards in many regions globally. In West Africa, soil moisture impacts provide a valuable source of predictability for mature storm hazards, but little is known about mature storm sensitivity to soil moisture in other climatic regions. Here we use a storm track dataset, satellite observations and reanalysis fields to investigate the response of mature storms to soil moisture in seven global storm hotspots—West Africa, India, South America, South Africa, Australia and the United States Great Plains. We demonstrate that mesoscale soil moisture gradients (~500 km) can enhance storms by driving increased vertical wind shear conditions, a crucial ingredient for storm organization, through the strengthening of atmospheric temperature gradients. This is evidenced by a 10–30% increase in precipitation feature size and rainfall for the largest storms on days with favourable soil moisture gradients compared with unfavourable gradients. Global simulations confirm that soil moisture gradients influence wind shear. The results demonstrate the importance of soil moisture feedbacks for accurate forecasting of mesoscale convective systems and future projections of extreme events under climate change.

54 ENVIRONMENTAL SCIENCES↗