Stochastic Cellular Automata Modeling of CO2 Hydrate Growth and Morphology

A physical-basedcomputational modeling framework combiningthe Monte Carlo (MC) and cellular automata (CA) techniques providesinsight into CO2 hydrate crystal growth and morphology. Carbon dioxide (CO2) hydrates are important in a diverserange of applications and technologies in the environmental and energyfields. The development of such technologies relies on fundamentalunderstanding, which necessitates not only experimental but also computationalstudies of the growth behavior of CO2 hydrates and thefactors affecting their crystal morphology. As experimental observationsshow that the morphology of CO2 hydrate particles differsdepending on growth conditions, a detailed understanding of the relationbetween the hydrate structure and growth conditions would be helpful.To this end, this work adopts a modeling approach based on hybridprobabilistic cellular automata to investigate variations in CO2 hydrate crystal morphology during hydrate growth from stagnantliquid water presaturated with CO2. The model, which usesfree energy density profiles as inputs, correlates the variationsin growth morphology to the system subcooling Delta T, i.e., the temperature deficiency from the triple CO2-hydrate-water equilibrium temperature under a givenpressure, and properties of the growing hydrate-water interface, suchas surface tension and curvature. The model predicts that when Delta T is large, parabolic needle-like or dendrite crystalsemerge from planar fronts that deform and lose stability. In agreementwith chemical diffusion-limited growth, the position of such planarfronts versus time follows a power law. In contrast, the tips of theemerging parabolic crystals steadily grow in proportion to time. Themodeling framework is computationally fast and produces complex growthmorphology phenomena under diffusion-controlled growth from simple,easy-to-implement rules, opening the way for employing it in multiscalemodeling of gas hydrates.

Automated summary

A physical-based computational modeling framework combining the Monte Carlo and cellular automata techniques provides insight into CO2 hydrate crystal growth and morphology. CO2 hydrates are important in a diverse range of applications and technologies in the environmental and energy fields. The development of such technologies relies on fundamental understanding, which necessitates not only experimental but also computational studies of the growth behavior of CO2 hydrates and the factors affecting their crystal morphology.