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This data upload is part of a broader study that explores a wide range of simulations conducted using the LBPM simulation software (available at https://github.com/OPM). The dataset consists of extensive GPU-based LBM (Lattice Boltzmann Method) simulations for determining relative permeability and pore-scale morphologcial properties, representing one of the largest open-access datasets of its kind. The data are structured in CSV files and R scripts are available for post analysis.These simulations, provide critical insights into the influence of wettability, multiscale system upscaling, and ganglion dynamics on multiphase flow behavior. The dataset is not only valuable for traditional analysis but also serves as a foundation for emerging techniques such as deep learning-assisted time-stepping, phase morphological generation, and predictive modeling of relative permeability and/or phase morphological evolution. By offering this comprehensive dataset to the porous media research community, we aim to foster broad collaboration and support innovative future research directions.
Fluid dynamics, Computational fluid dynamics
Fluid dynamics, Computational fluid dynamics
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |