A Compositional Global Implicit Approach for Modeling Coupled Multicomponent Reactive Transport
Bibliographic record
Abstract
Abstract Reactive transport modeling has become widely used to help improve understanding of hydrogeochemical processes from the pore scale to the watershed scale. In recent years, the scope of reactive transport applications has increased toward a higher level of complexity and process coupling. For example, the production and consumption of water as well as porosity evolution associated with the dissolution and precipitation of hydrated minerals can impact system evolution. Waste rock weathering, carbon sequestration, or the degradation of engineered barriers in radioactive waste repositories all constitute applications in which geochemistry and hydrodynamics can strongly influence each other. For these purposes, the traditional formulation of reactive transport simulators, which decouples groundwater flow and reactive transport processes, is limited. We present a global implicit compositional approach, which integrates the flow processes directly into the reactive transport and geochemical framework. This approach solves the flow field implicitly with the reactive transport equations, simultaneously accounting for water consumption and production due to geochemical reactions. Applications show that the model allows tackling complex reactive transport problems while accounting for intra‐aqueous reactions, redox reactions, and reactions involving mass transfer with the gas and solid phases. The presented simulations also demonstrate that the compositional and traditional approaches yield similar results for complex geochemical systems with relatively low reactivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".