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Coupled multiphysical model for investigation of influence factors in the application of microbially induced calcite precipitation

多物理 方解石 有限元法 磁导率 材料科学 计算机科学 生物系统 工程类 地质学 矿物学 化学 结构工程 生物 生物化学
作者
Xuerui Wang,Pavan Kumar Bhukya,Dali Naidu Arnepalli,Shuang Chen
出处
期刊:Journal of rock mechanics and geotechnical engineering [Elsevier BV]
卷期号:16 (6): 2232-2249 被引量:4
标识
DOI:10.1016/j.jrmge.2024.03.007
摘要

The study presents a comprehensive coupled thermo-bio-chemo-hydraulic (T-BCH) modeling framework for stabilizing soils using microbially induced calcite precipitation (MICP). The numerical model considers relevant multiphysics involved in MICP, such as bacterial ureolytic activities, biochemical reactions, multiphase and multicomponent transport, and alteration of the porosity and permeability. The model incorporates multiphysical coupling effects through well-established constitutive relations that connect parameters and variables from different physical fields. It was implemented in the open-source finite element code OpenGeoSys (OGS), and a semi-staggered solution strategy was designed to solve the couplings, allowing for flexible model settings. Therefore, the developed model can be easily adapted to simulate MICP applications in different scenarios. The numerical model was employed to analyze the effect of various factors, including temperature, injection strategies, and application scales. Besides, a T-BCH modeling study was conducted on the laboratory-scale domain to analyze the effects of temperature on urease activity and precipitated calcium carbonate. To understand the scale dependency of MICP treatment, a large-scale heterogeneous domain was subjected to variable biochemical injection strategies. The simulations conducted at the field-scale guided the selection of an injection strategy to achieve the desired type and amount of precipitation. Additionally, the study emphasized the potential of numerical models as reliable tools for optimizing future developments in field-scale MICP treatment. The present study demonstrates the potential of this numerical framework for designing and optimizing the MICP applications in laboratory-, prototype-, and field-scale scenarios.

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