@article{fdi:010092893,
  title = {{A} voxel-based approach for simulating microbial decomposition in soil : comparison with {LBM} and improvement of morphological models},
  author = {{K}lai, {M}. and {M}onga, {O}livier and {J}ouini, {M}. {S}. and {P}ot, {V}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his paper deals with the computational modeling of biological dynamics in soil using an exact micro-scale pore space description from 3{D} {C}omputed {T}omography ({CT}) images. {W}ithin this context, computational costs and storage requirements constitute critical factors for running simulations on large datasets over extended periods. {I}n this research, we represent the pore space by a graph of voxels ({V}oxel {G}raph-{B}ased {A}pproach, {VGA}) and model transport in fully saturated conditions (two-phase system) using {F}ick's law and coupled diffusion with biodegradation processes to simulate microbial decomposition in soil. {T}o significantly decrease the computational time of our approach, the diffusion model is solved by means of {E}uler discretization schemes, along with parallelization strategies. {W}e also tested several numerical strategies, including implicit, explicit, synchronous, and asynchronous schemes. {T}o validate our {VGA}, we compare it with {LB}io{S}, a 3{D} model that integrates diffusion (via the {L}attice {B}oltzmann method) with biodegradation, and {M}osaic, a {P}ore {N}etwork {G}eometrical {M}odelling ({PNGM}) which represents the pore space using geometrical primitives. {O}ur method yields result similar to those of {LB}io{S} in a quarter of the computing time. {W}hile slower than {M}osaic, it is more accurate and requires no calibration. {A}dditionally, we show that our approach can improve {PNGM}-based simulations by using a machine-learning approach to approximate diffusional conductance coefficients.},
  keywords = {},
  booktitle = {},
  journal = {{PL}os {O}ne},
  volume = {20},
  numero = {3},
  pages = {e0313853 [25 ]},
  ISSN = {1932-6203},
  year = {2025},
  DOI = {10.1371/journal.pone.0313853},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092893},
}