Approximately consistent SPH simulations of the anisotropic dispersion of a contaminant plume

Klapp, Jaime; Sigalotti, Leonardo Di G.; Alvarado-Rodríguez, Carlos E.; Rendón, Otto; Díaz–Damacillo, Lamberto

doi:10.1007/s40571-022-00461-1

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…By interpolation, these properties are estimated from all neighbouring particles within the range of this function, the so-called smoothing kernel. The collective motion of all particles then describes the flow pattern [173][174][175].…”

Section: Dispersionmentioning

confidence: 99%

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik,

Pogrzeba

2024

Applied Sciences

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The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.

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Section: Dispersionmentioning

confidence: 99%