A Voxel-based Approach for Simulating Microbial Decomposition in Soil: Comparison with LBM and Improvement of Morphological Models

TL;DR

This paper introduces a voxel-based computational method for simulating microbial decomposition in soil, offering comparable accuracy to LBM with significantly reduced computation time and enhancing PNGM models through a new theoretical framework.

Contribution

A novel voxel-based simulation approach for microbial decomposition that improves accuracy over existing models and reduces computational costs, with a new theoretical framework for PNGM enhancement.

Findings

Achieved comparable results to LBM with only one-quarter of the computing time.

Proposed method is more accurate than Mosaic without calibration.

Presented a framework to improve PNGM simulations using stochastic gradient descent.

Abstract

This study presents a new computational approach for simulating the microbial decomposition of organic matter, from 3D micro-computed tomography (micro-CT) images of soil. The method employs a valuated graph of connected voxels to simulate transformation and diffusion processes involved in microbial decomposition within the complex soil matrix. The resulting model can be adapted to simulate any diffusion-transformation processes in porous media. We implemented parallelization strategies and explored different numerical methods, including implicit, explicit, synchronous, and asynchronous schemes. To validate our method, we compared simulation outputs with those provided by LBioS and by Mosaic models. LBioS uses a lattice-Boltzmann method for diffusion and Mosaic takes benefit of Pore Network Geometrical Modelling (PNGM) by means of geometrical primitives such as spheres and ellipsoids. This approach achieved comparable results to traditional LBM-based simulations, but required only one-fourth of the computing time. Compared to Mosaic simulation, the proposed method is slower but more accurate and does not require any calibration. Furthermore, we present a theoretical framework and an application example to enhance PNGM-based simulations. This is accomplished by approximating the diffusional conductance coefficients using stochastic gradient descent and data generated by the current approach.