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Multi-Scale Computational Methods for Reactive Transport of in situ Uranium Leaching: Pore Structure Evolution, Multi-Physics Coupling, and Numerical Simulations.

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Bibliographic Details
Title:	Multi-Scale Computational Methods for Reactive Transport of in situ Uranium Leaching: Pore Structure Evolution, Multi-Physics Coupling, and Numerical Simulations.
Authors:	Hou, Shanshan¹ (AUTHOR), Zeng, Sheng¹ (AUTHOR) usczengs@126.com, Yang, Yugui² (AUTHOR), Song, Jiayin¹ (AUTHOR), Sun, Bing³ (AUTHOR)
Source:	Archives of Computational Methods in Engineering. Sep2026, Vol. 33 Issue 7, p9475-9520. 46p.
Subjects:	Multiscale modeling, Reactive flow, Porosity, Artificial intelligence, Computer simulation, Dissolution (Chemistry), Leaching
Abstract:	In situ leaching (ISL) uranium mining involves intricate multi-scale dynamics spanning nine orders of magnitude, multi-physics coupling among hydraulic, transport, chemical, thermal, and mechanical fields, as well as strong nonlinearity arising from dissolution-precipitation feedback. This review systematically examines recent advances in three fundamental aspects of ISL uranium mining: characterization and evolution of porous media structures, multi-physics coupling mechanisms, and numerical simulation of reactive transport processes. Methodologies for pore structure characterization are outlined, dual fractal characteristics and their relationships with permeability are analyzed, and mechanisms governing pore evolution including dissolution-precipitation competition, particle migration, clay swelling, and gas blockage are elucidated. Seepage theories for multiple flow regimes are reviewed, reactive transport coupling models integrating five physical fields are examined, and reaction kinetics including rate laws, Arrhenius temperature dependence, and sorption models are summarized with dimensionless parameters characterizing dominant transport mechanisms. Multi-scale modeling approaches from pore-scale to field-scale are compared, major simulation platforms are evaluated with model selection criteria, and cross-scale correlation techniques including the embedded multi-scale method are discussed. Additionally, emerging applications of artificial intelligence technologies including machine learning surrogates, deep learning, physics-informed neural networks, and digital twins are highlighted for process optimization and environmental monitoring. Finally, key technical challenges and future research directions are identified. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In situ leaching (ISL) uranium mining involves intricate multi-scale dynamics spanning nine orders of magnitude, multi-physics coupling among hydraulic, transport, chemical, thermal, and mechanical fields, as well as strong nonlinearity arising from dissolution-precipitation feedback. This review systematically examines recent advances in three fundamental aspects of ISL uranium mining: characterization and evolution of porous media structures, multi-physics coupling mechanisms, and numerical simulation of reactive transport processes. Methodologies for pore structure characterization are outlined, dual fractal characteristics and their relationships with permeability are analyzed, and mechanisms governing pore evolution including dissolution-precipitation competition, particle migration, clay swelling, and gas blockage are elucidated. Seepage theories for multiple flow regimes are reviewed, reactive transport coupling models integrating five physical fields are examined, and reaction kinetics including rate laws, Arrhenius temperature dependence, and sorption models are summarized with dimensionless parameters characterizing dominant transport mechanisms. Multi-scale modeling approaches from pore-scale to field-scale are compared, major simulation platforms are evaluated with model selection criteria, and cross-scale correlation techniques including the embedded multi-scale method are discussed. Additionally, emerging applications of artificial intelligence technologies including machine learning surrogates, deep learning, physics-informed neural networks, and digital twins are highlighted for process optimization and environmental monitoring. Finally, key technical challenges and future research directions are identified. [ABSTRACT FROM AUTHOR]
ISSN:	11343060
DOI:	10.1007/s11831-026-10519-5