Turbulent Flow Characteristics Over Offset Wall Confined Columns in a Channel at Low Reynolds Numbers
Bibliographic record
Abstract
The current study is focused on the flow through offset, wall confined vertical inserts in a channel. The columns are intended to act as the thermal storage media, which continuously exchange heat with the channel fluid to regulate it thermally. These columns could, for example, be filled with a phase change material (PCM) for passive thermal regulation, or have hot or cold fluid pumped through them for active thermal regulation. The current study has two parts: (1) the flow characterization without heat transfer, and (2) flow characterization during thermal exchange with a PCM used for regulation of bulk fluid temperature. The work presented here is focused only on the first part of the study. The experiments were conducted in a narrow channel, with water as the working fluid. Two geometries of the vertical columns (circular and square) and two offset lengths were considered. For each configuration, experiments were conducted at Reynolds numbers of 20, 50 and 90 (based of the column’s characteristic length). Particle image velocimetry was used to measure the two-dimensional velocity field in a horizontal plane at multiple regions of interest along the length of the channel to characterize the flow passing over columns. The results indicate vortex shedding at the two higher Reynolds numbers. The generation, magnitude and decay rate of turbulent energy is shown to have an offset dependency at Re = 90, but a column shape dependency at Re = 50. The mean flow has a shape dependency due to the difference in separation point over the square and circular columns.
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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 | 0.001 | 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".