Abstract Spatial Markov random walk models (SMM) have been demonstrated to accurately predict conservative solute transport across a wide range of hydro-geological systems, with recent advances enabling the SMM to model systems with linear kinetic reactive processes. However, the proposed reactive SMM’s applicability is limited to systems that can be partitioned into a series of identical periodic cells where travel times across cells are highly correlated to the solute’s entrance position at the cell inlet. In real geologic settings, the spatial layout and size of grains varies through space, decorrelating the relationship between travel time and transverse position. Here, we generalize previous SMM implementations and implement a Bernoulli CTRW, where transport behavior can be captured in disordered and non-periodic porous media. We validate our upscaled model predictions with results from direct numerical simulation of transport in a 2D porous column that cannot be partitioned into identical periodic elements. We parameterize our model based on a subset of simulation statistics and explore how model accuracy changes due to our sampling method. This finding yields important insights for optimizing efficiency of the upscaled transport model parameterization and can guide field sampling of geological structures as well as multiscale investigation of laboratory observations.

中文翻译：

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无序非周期性多孔域中吸附溶质的放大传输

摘要 空间马尔可夫随机游走模型 (SMM) 已被证明可以准确预测各种水文地质系统中的保守溶质输运，最近的进展使 SMM 能够对具有线性动力学反应过程的系统进行建模。然而，所提出的反应性 SMM 的适用性仅限于可以划分为一系列相同周期单元的系统，其中跨单元的旅行时间与单元入口处溶质的入口位置高度相关。在真实的地质环境中，颗粒的空间布局和大小随空间变化，从而消除了旅行时间和横向位置之间的关系。在这里，我们概括了以前的 SMM 实现并实现了伯努利 CTRW，其中可以在无序和非周期性多孔介质中捕获传输行为。我们用二维多孔柱中输运的直接数值模拟结果验证了我们的放大模型预测，该柱不能分成相同的周期元素。我们基于模拟统计的子集参数化我们的模型，并探索模型精度如何因我们的采样方法而变化。这一发现为优化放大传输模型参数化的效率提供了重要的见解，可以指导地质结构的现场采样以及实验室观察的多尺度调查。