Numerical Simulation of Fluid Flow and Oxygen Transport in the Tube Flow Cells Containing Biofilms
Bibliographic record
Abstract
The hydrodynamics in flow systems is known to induce phenotypic changes associated with bacterial biofilms, including increased tolerance to antimicrobial agents and biocides. Results obtained in flow cells commonly used in biological and medical studies on the influence of flow on biofilm behavior and antimicrobial susceptibility are sometimes contradictory. It is thus hypothesized that discrepancies in the results may be related to the flow cell geometry. In this study, the shear stress distribution and substrate concentration were numerically simulated inside long rectangular and square tubes. The fluid was Newtonian and a uniform distribution of biofilms, which consume the substrate from the medium, was assumed on the walls. The consumption of oxygen by biofilms was assumed to follow the Monod kinetics. The effects of flow velocity, flow cell geometry, and substrate diffusivity on wall shear stress and substrate concentration distributions were investigated. Based on simulation results, differences observed in the morphology and response of biofilms can be directly related to hydrodynamic changes caused by the flow cell configuration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".