Bibliographic record
Abstract
The purpose of this thesis is to study the problem of a low Reynolds number\nswimmer that is in very close proximity to a wall or solid boundary in a non-\nNewtonian fluid. We assume that it moves by propagating waves down its length\nin one direction, creating a thrust and therefore propelling it in the opposite\ndirection. We model the swimmer as an infinite, inextensible waving sheet.\nWe consider two main cases of this swimming sheet problem. In the first\ncase, the type of wave being propagated down the length of the swimmer is\nspecified. We compare the swimming speeds of viscoelastic shear thinning,\nshear thickening and Newtonian fluids for a fixed propagating wave speed. We\nthen compare the swimming speeds of these same fluids for a fixed rate of work\nper wavelength. In the latter situation, we find that a shear thinning fluid\nalways yields the fastest swimming speed regardless of the amplitude of the\npropagating waves. We conclude that a shear thinning fluid is optimal for the\nswimmer. Analytical results are obtained for various limiting cases. Next, we\nconsider the problem with a Bingham fluid. Yield surfaces and flow profiles are\nobtained.\nIn the second case, the forcing along the length of the swimmer is specified,\nbut the shape of the swimmer is unknown. First, we solve this problem for a\nNewtonian fluid. Large amplitude forcing yields a swimmer shape that has a\nplateau region following by a large spike region. It is found that there exists\nan optimal forcing that will yield a maximum swimming speed. Next, we solve\nthe problem for moderate forcing amplitudes for viscoelastic shear thickening\nand shear thinning fluids. For a given forcing, it is found that a shear thinning\nfluid yields the fastest swimming speed when compared to a shear thickening\nfluid and a Newtonian fluid. The difference in swimming speeds decreases as\nthe bending stiffness of the swimmer increases.
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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 | 0.000 | 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 | 0.000 |
| 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".