A Modified Time Domain Random Walk Approach for Simulating Colloid Behavior in Fractures: Method Development and Verification
Bibliographic record
Abstract
Abstract Colloids are ubiquitous in groundwater systems, and understanding their behavior is of critical importance as they can pose a threat to human and environmental health. Extensive research has been conducted to model colloid behavior in fractures leading to the development of a number of analytical solutions, albeit for single fractures. However, the application of these analytical solutions at the network scale has not yet been established; thus, fracture networks are modeled using numerical techniques that are verified in single fractures first. Time domain random walk (TDRW) is a Lagrangian‐based approach originally designed to simulate solute transport in single fractures considering advection, dispersion, and matrix diffusion. The present study develops a modified TDRW approach (MTDRW) to consider colloid‐specific transport mechanisms based on an analytical solution describing colloid behavior in single fractures. The MTDRW approach was validated through simulating the behavior of (i) monodisperse colloids in a synthetic, single fracture with and without matrix diffusion; (ii) polydisperse colloids in a synthetic, single fracture with impermeable matrix; and (iii) monodisperse colloids in a synthetic impermeable fracture network. In all three cases, the MTDRW approach replicated the results of analytical solutions in single fractures and the semianalytical solution in fracture networks. The MTDRW approach is expected to enhance the reliability of colloid transport modeling due to its capacity to simulate the physiochemical heterogeneity across the network. This is required to (i) evaluate the aquifer vulnerability to colloid migration; (ii) predict the aquifer resilience under contamination events; and (iii) develop effective planning, management, and remediation strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".