Large‐eddy simulation of oxygen transfer to organic sediment beds
Bibliographic record
Abstract
We have developed a model for dissolved oxygen (DO) transfer from water to underlying organic sediment beds. The model couples large‐eddy simulation (LES) of turbulent transport in the water column, a biogeochemical model for DO transport and consumption in the sediment, and Darcy's Law for the pore water‐driven transport. The model highlights the spatial and temporal relationship between the turbulent bursting events, the near‐wall transport of DO, and the response of the sediment layer. The numerical results—compared to data from laboratory experiments—stress the importance of analyzing instantaneous transport events (not reproducible in a Reynolds‐averaged Navier‐Stokes model) to better characterize and model the processes that lead to oxygen depletion in the sediment layer. The model's results are compared against experimental data and the sensitivity to the governing parameters has been tested. As the current velocity increases, the sediment‐oxygen demand (SOD) increases more slowly than the friction velocity at the wall, in accordance with classic heat‐and‐mass transfer laws. The overall SOD is approximately proportional to the bacterial content of the sediment layer. The predicted mean advective flux in the porous medium and across the sediment‐water interface is negligible compared to the total dispersive flux, for permeabilities typical of the sediments used in the experiments (10 −7 and 10 −6 cm 2 ). Higher permeabilities (10 −5 cm 2 ) appear to yield results not consistent with experimental data. This computational tool will contribute toward the design of a process‐oriented parametrization for the SOD, currently missing in oceanographic applications, that can be easily extended to the transport of other bio‐limiting substances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".