English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Determining the pressure differential required to achieve a desired flow ratein a porous medium requires solving Darcy's law, a Laplace-like equation, witha spatially varying tensor permeability. In various scenarios, the permeabilitycoefficient is sampled at high spatial resolution, which makes solving Darcy'sequation numerically prohibitively expensive. As a consequence, much effort hasgone into creating upscaled or low-resolution effective models of thecoefficient while ensuring that the estimated flow rate is well reproduced,bringing to fore the classic tradeoff between computational cost and numericalaccuracy. Here we perform a statistical study to characterize the relativesuccess of upscaling methods on a large sample of permeability coefficientsthat are above the percolation threshold. We introduce a new technique based onMode-Elimination Renormalization-Group theory (MG) to build coarse-scalepermeability coefficients. Comparing the results with coefficients upscaledusing other methods, we find that MG is consistently more accurate,particularly so due to its ability to address the tensorial nature of thecoefficients. MG places a low computational demand, in the manner that we haveimplemented it, and accurate flow-rate estimates are obtained when usingMG-upscaled permeabilities that approach or are beyond the percolationthreshold.

Renormalization Group theory outperforms other approaches in statistical comparison between upscaling techniques for porous media