Investigation of a modified sequential iteration approach for solving coupled reactive transport problems
Bibliographic record
Abstract
When contaminants enter the soil or groundwater, they may interact physically, geochemically and biochemically with the native water, microorganisms and solid matrix. A realistic description of a reactive transport regime that includes these processes requires joint consideration of multiple chemical species. Currently there are three common numerical approaches for coupling multispecies reaction and solute transport: the one-step approach, the sequential non-iterative approach (SNIA), and the sequential iterative approach (SIA). A modification of the SNIA method is the Strang-splitting method. In this study, a new modified sequential iteration approach (MSIA) for solving multicomponent reactive transport in steady state groundwater flow is presented. This coupling approach has been applied to two realistic reactive transport problems and its performance compared with the SIA and the Strang-splitting methods. The comparison shows that MSIA consistently converges faster than the other two coupling schemes. For the simulation of nitrogen and related species transport and reaction in a riparian aquifer, the total CPU time required by MSIA is only about 38% of the total CPU time required by the SIA, and only 50% of the CPU time required by the Strang-splitting method. The test problem results indicate that the SIA has superior accuracy, while the accuracy of MSIA is marginally better than that of the Strang-splitting method. The overall performance of MSIA is considered to be good, especially for simulations in which computational time is a critical factor. Copyright © 2005 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".