As LiCoO 2 cathodes are charged, delithiation of the LiCoO 2 active material leads to an increase in the lattice spacing, causing swelling of the particles. When these particles are packed into a bicontinuous, percolated network, as is the case in a battery electrode, this swelling leads to the generation of significant mechanical stress. In this study we performed coupled electrochemical-mechanical simulations of the charging of a LiCoO 2 cathode in order to elucidate the mechanisms of stress generation and the effect of charge rate and microstructure on these stresses. Energy dispersive spectroscopy combined with scanning electron microscopy imaging was used to create 3D reconstructions of a LiCoO 2 cathode, and the Conformal Decomposition Finite Element Method is used to automatically generate computational meshes on this reconstructed microstructure. Replacement of the ideal solution Fickian diffusion model, typically used in battery simulations, with a more general non-ideal solution model shows substantially smaller gradients of lithium within particles than is typically observed in the literature. Using this more general model, lithium gradients only appear at states of charge where the open-circuit voltage is relatively constant. While lithium gradients do affect the mechanical stress state in the particles, the maximum stresses are always found in the fully-charged state and are strongly affected by the local details of the microstructure and particle-to-particle contacts. These coupled electrochemical-mechanical simulations begin to yield insight into the partitioning of volume change between reducing pore space and macroscopically swelling the electrode. Finally, preliminary studies that include the presence of the polymeric binder suggest that it can greatly impact stress generation and that it is
Battery performance, while observed at the macroscale, is primarily governed by the bicontinuous mesoscale network of the active particles and a polymeric conductive binder in its electrodes. Manufacturing processes affect this mesostructure, and therefore battery performance, in ways that are not always clear outside of empirical relationships. Directly studying the role of the mesostructure is difficult due to the small particle sizes (a few microns) and large mesoscale structures. Mesoscale simulation, however, is an emerging technique that allows the investigation into how particle-scale phenomena affect electrode behavior. In this manuscript, we discuss our computational approach for modeling electrochemical, mechanical, and thermal phenomena of lithium-ion batteries at the mesoscale. We review our recent and ongoing simulation investigations and discuss a path forward for additional simulation insights.
Condensation is a physical process that occurs when a vapor is cooled and/or compressed to its saturation limit. Condensation becomes important in a variety of engineering applications such as in heat exchangers used for distillation purposes. In such instances, higher condensation efficiencies are desirable. Research to improve condensation has focused on dropwise condensation as it has been shown that it can be significantly more efficient than filmwise condensation. Recent investigations of dropwise condensation on nanostructured surfaces suggest that enhanced dropwise condensation can be attained as the average droplet sizes are reduced for clusters growing through dropwise condensation. This, in turn, significantly enhances the heat transfer coefficients of dropwise condensation. This paper summarizes a computational model developed to explore the mechanisms leading to this enhanced dropwise condensation. A Direct Simulation Monte Carlo (DSMC) approach is used here to investigate the mechanisms and limitations of enhanced dropwise condensation for these surfaces aiming to reduce the average droplet sizes of condensation. For computational purposes, several idealizations are assumed by the model, which include: (1) The condensation droplet clusters are assumed to have uniform size, corresponding to an average droplet size observed in actual dropwise condensation scenarios; (2) Due to the assumed uniform droplet distribution, symmetry can be observed from the droplet cluster, so a small but symmetrical cross section of the droplet distribution is used for the computational domain; and (3) Supersaturated steam condensing on a cold wall is assumed for most of the simulations. The mechanisms at play that are deliberately explored are: (1) The effects of surface wettability by using a model that considers droplet conduction variations with varying contact angle; (2) The changes of interfacial resistance with droplet curvature by introducing a surface tension model based on the Tolman length; and (3) The dynamic interactions between neighboring droplets by choosing our computational domain to be a symmetrical cross section that encompasses surrounding droplets in an appropriate fashion. The ambient conditions that were investigated were: (1) Varying atmospheric pressure; (2) Varying amounts of wall subcooling for the droplets; (3) Varying accommodation for water molecules condensing on the droplet; and (4) The introduction of air into the assumed supersaturated steam condensing on the cold wall. To investigate the overall and combined effects of the aforementioned mechanisms on enhanced dropwise condensation through reduced droplet sizes, the simulations were run for droplets with radii between 1 micrometer down to 5 nanometers. The model predictions indicate that the larger droplet transport trend of increasing heat transfer with decreasing droplet sizes breaks down as droplet sizes become smaller due to more prominence of the mechanisms hindering condensation for the reduced droplet sizes. As the model breaks down, a peak heat transfer is reached, and heat transfer is further reduced as the average droplet sizes continue to decrease. The predictions of this particular DSMC model are compared to previous work investigating similar effects. The implications of our observations and potential impact to current and future research in the area is discussed in detail.
Experimental studies of dropwise condensation have generally indicated that higher heat transfer coefficients correspond to smaller mean sizes for droplets growing through condensation on the surface. Recent investigations of dropwise condensation on nanostructured surfaces suggest that optimizing the design of such surfaces can push mean droplet sizes down to smaller values and significantly enhance heat transfer. This paper summarizes a theoretical exploration of the limits of heat transfer enhancement that can be achieved by pushing mean droplet size to progressively smaller sizes. A model analysis is developed that predicts transport near clusters of water droplets undergoing dropwise condensation. The model accounts for interfacial tension effects on thermodynamic equilibrium and noncontinuum transport effects, which become increasingly important as droplet size becomes progressively smaller. In this investigation, the variation of condensing heat transfer coefficient for droplet clusters of different size was explored for droplet diameters ranging from hundreds of microns to tens of nanometers. The model predictions indicate that the larger droplet transport trend of increasing heat transfer coefficient with decreasing mean droplet size breaks down as droplet size becomes smaller. The model further predicts that as drop size becomes smaller, a peak heat transfer coefficient is reached, beyond which the coefficient drops as the size continues to diminish. This maximum heat transfer coefficient results from the increasing importance of surface tension effects and non-continuum effects as droplet size becomes smaller. The impact of these predictions on the interpretation of dropwise condensation heat transfer data, and the implications for design of nanostructured surfaces to enhance dropwise condensation are discussed in detail.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.