A model for the permeability of coffee pucks validated using X-ray computed micro-tomography

Abstract Tuning the permeability of beds of ground coffee is central to making good espresso. Here, we develop a theoretical framework for such a permeability model, which depends principally on the pore volume fraction that is connected between the grains ϕp and the specific surface area of the interfaces between liquid and coffee grains, s. We test our theoretical predictions by using lattice-Boltzmann simulations of fluid flow through three-dimensional domains obtained via X-ray computed micro-tomography (XCT) of real packed ground coffee. Our tomography is performed on two different roasted coffees (‘Tumba’ from Rwanda and ‘Guayacán’ from Colombia) ground to one of 11 grind settings using a Mahlkönig grinder, ranging from very fine to coarse grinds. We find excellent agreement with our percolation theory, suggesting a practical way to relate grind size and packing fraction to the specific surface area to predict coffee permeability. We discuss this model in the context of optimal espresso recipe design and the effects of coffee dose to define a Forchheimer number that can be used to predict the onset of inertial flow in coffee.

Authors

Institutions

Publication Details

Journal
Royal Society Open Science
Published
2026-04-01
DOI
https://doi.org/10.1098/rsos.252031
Primary Topic
Coffee research and impacts
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A model for the permeability of coffee pucks validated using X-ray computed micro-tomography

Fabian B. Wadsworth, Jackie E. Kendrick, Hannah M. Buckland, Jason P. Coumans et al.
Royal Society Open Science
Coffee research and impacts
article

A model for the permeability of coffee pucks validated using X-ray computed micro-tomography

Fabian B. Wadsworth, Jackie E. Kendrick, Hannah M. Buckland, Jason P. Coumans, Katherine J. Dobson, Christopher H. Hendon, Anna Theurel, Jingwei Zhang, Jérémie Vasseur, M. J. Heap, Yan Lavallée, Jonathan Gagné
article en

Abstract

Abstract Tuning the permeability of beds of ground coffee is central to making good espresso. Here, we develop a theoretical framework for such a permeability model, which depends principally on the pore volume fraction that is connected between the grains ϕp and the specific surface area of the interfaces between liquid and coffee grains, s. We test our theoretical predictions by using lattice-Boltzmann simulations of fluid flow through three-dimensional domains obtained via X-ray computed micro-tomography (XCT) of real packed ground coffee. Our tomography is performed on two different roasted coffees (‘Tumba’ from Rwanda and ‘Guayacán’ from Colombia) ground to one of 11 grind settings using a Mahlkönig grinder, ranging from very fine to coarse grinds. We find excellent agreement with our percolation theory, suggesting a practical way to relate grind size and packing fraction to the specific surface area to predict coffee permeability. We discuss this model in the context of optimal espresso recipe design and the effects of coffee dose to define a Forchheimer number that can be used to predict the onset of inertial flow in coffee.

Royal Society Open ScienceVol. 13(4)
University of Oregon (US), University of Strathclyde (GB), Institut Universitaire de France (FR), Durham University (GB), Espace pour la vie (CA), University of Bristol (GB), Lancaster University (GB), Université de Strasbourg (FR), Université de Montréal (CA), Ludwig-Maximilians-Universität München (DE)
Natural Environment Research Council
Openalex Percentile: Top 36%
Coffee research and impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.