Biological implications of the hydrodynamics of swimming at or near the surface and in shallow water
Bibliographic record
Abstract
The origins and effects of wave drag at and near the surface and in shallow water are discussed in terms of the dispersive waves generated by streamlined technical bodies of revolution and by semi-aquatic and aquatic animals with a view to bearing on issues regarding the design and function of autonomous surface and underwater vehicles. A simple two-dimensional model based on energy flux, allowing assessment of drag and its associated wave amplitude, is applied to surface swimming in Lesser Scaup ducks and is in good agreement with measured values. It is argued that hydrodynamic limitations to swimming at speeds associated with the critical Froude number ( approximately 0.5) and hull speed do not necessarily set biological limitations as most behaviours occur well below the hull speed. From a comparative standpoint, the need for studies on the hull displacement of different forms is emphasized. For forms in surface proximity, drag is a function of both Froude and Reynolds numbers. Whilst the depth dependence of wave drag is not particularly sensitive to Reynolds number, its magnitude is, with smaller and slower forms subject to relatively less drag augmentation than larger, faster forms that generate additional resistance due to ventilation and spray. A quasi-steady approach to the hydrodynamics of swimming in shallow water identifies substantial drag increases relative to the deeply submerged case at Froude numbers of about 0.9 that could limit the performance of semi-aquatic and aquatic animals and autonomous vehicles. A comparative assessment of fast-starting trout and upside down catfish shows that the energy losses of fast-starting fish are likely to be less for fish in surface proximity in deep water than for those in shallow water. Further work on unsteady swimming in both circumstances is encouraged. Finally, perspectives are offered as to how autonomous surface and underwater vehicles in surface proximity and shallow water could function to avoid prohibitive hydrodynamic resistance, thereby increasing their operational life.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".