High-Schmidt-number mass transport mechanisms from a turbulent flow to absorbing sediments
Bibliographic record
Abstract
We have investigated the mechanisms involved in dissolved oxygen (DO) transfer from a turbulent flow to an underlying organic sediment bed populated with DO-absorbing bacteria. Our numerical study relies on a previously developed and tested computational tool that couples a bio-geochemical model for the sediment layer and large-eddy simulation for transport on the water side. Simulations have been carried out in an open channel configuration for different Reynolds numbers (Reτ = 180–1000), Schmidt numbers (Sc = 400–1000), and bacterial populations (χ* = 100–700 mg l−1). We show that the average oxygen flux across the sediment-water interface (SWI) changes with Reτ and Sc, in good agreement with classic heat-and-mass-transfer parametrizations. Time correlations at the SWI show that intermittent peaks in the wall-shear stress initiate the mass transfer and modulate its distribution in space and time. The diffusive sublayer acts as a de-noising filter with respect to the overlying turbulence; the instantaneous mass flux is not affected by low-amplitude background fluctuations in the wall-shear stress but, on the other hand, it is receptive to energetic and coherent near-wall transport events, in agreement with the surface renewal theory. The three transport processes involved in DO depletion (turbulent transport, molecular transport across the diffusive sublayer, and absorption in the organic sediment layer) exhibit distinct temporal and spatial scales. The rapidly evolving near-wall high-speed streaks transport patches of fluid to the edge of the diffusive sublayer, leaving slowly regenerating elongated patches of positive DO concentration fluctuations and mass flux at the SWI. The sediment surface retains the signature of the overlying turbulent transport over long time scales, allowed by the slow bacterial absorption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".