Impacts of Instrumented Bottom Frame on Flow and Turbulence Measurements
Bibliographic record
Abstract
Abstract A series of laboratory experiments are carried out to demonstrate the impacts of instrumented bottom frame legs on flow and turbulence. The magnitudes of vertical velocity, turbulent kinetic energy, dissipation, and shear stress induced by the frame legs depend on several factors, including the diameter and number of the frame legs, distances between the legs and the observational location, and the magnitude of the incoming flow and its direction with respect to the layout of the frame. In situ observations were carried out near the mouth of the Yellow River using two acoustic Doppler velocimeters mounted on a bottom frame. The estimated vertical velocity and turbulent kinetic energy dissipation rate show a significant asymmetry with flood and ebb tidal flows. This asymmetry can be partly explained by the influences of the bottom frame legs. Finally, the design and deployment principles of bottom frames are discussed for the purpose of reducing the impacts of the frame legs. Significance Statement Instrumented bottom frames are widely used for observations in the oceanic bottom boundary layer and above. However, the impacts of frame legs on the observed flow and turbulence have rarely been investigated. A series of laboratory experiments demonstrate that frame legs can induce vertical flow and enhanced turbulence, and the magnitudes of these influences vary with the size and layout of the frame legs and the magnitude and direction of the background flow. The results of the laboratory experiments can partially explain an “asymmetry” behavior of the vertical flow and turbulent kinetic energy with the flood and ebb tidal flows, derived from in situ observations near the mouth of the Yellow River.
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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 | 0.001 |
| 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 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".