A 3D Free-Lagrangian method to simulate three-dimensional groundwater flow and mass transport
Bibliographic record
Abstract
This paper proposes a 3D Free-Lagrange algorithm for the numerical simulation of three dimensional groundwater flow and mass transport problems. The algorithm is particularly efficient for free-surface tracking, which is applicable to water table aquifers. The proposed method is based on 3D kinetic Voronoi and Delaunay data structures and can generate a mesh that accurately represents the geometry and the characteristics of 3D hydrogeological systems. This mesh is dynamic and can be interactively improved using topological-dynamic operations. Users can thus modify and adjust the density of the mesh elements locally and on-the-fly, for example by refining the mesh in areas with higher fluxes or concentration gradients, or reducing the element density in regions where fluxes and gradients are smaller. During a fluid flow simulation, the mesh moves and the topology, connectivity, and physical parameters (e.g. hydraulic conductivity, porosity, velocity and mass) of the mesh cells are updated at each time step. In addition, to solve the problems resulting from using a fixed time step, such as overshoots and undetected collisions, an event-driven algorithm has been designed to detect events by 3D Delaunay data structure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".