Backward-facing Step Flow in a Narrow Open Channel: Effects of Expansion Ratio and Reynolds Number
Bibliographic record
Abstract
Backward-facing step (BFS) flow phenomena is present in several engineering systems.While extensive research efforts have been invested in BFS flows, cases associated with narrow channels are relatively few.In this work, we examine the turbulent flow field of a narrow open-channel BFS to understand the effects of expansion ratio and Reynolds number variations.The physical system is modeled in an experimental facility consisting of a channel flume with a backward-facing step of height h installed in the upstream section of the flow.With an aspect ratio of 4, a narrow BFS flow configuration was achieved.By varying the flow depth for different test cases, BFS flows of expansion ratio (ER) of 1.25 and 1.50 were tested.Additionally, open-channel turbulent flow was conducted through the flume at various Reynolds number (Re) between 2900 and 11,000.A planar particle image velocimetry technique was used to capture the flow around the recirculation region.The results show that increasing Re by nearly two-fold leads to the evolution of a multi-centered primary recirculation bubble and a secondary corner bubble; while maintaining the reattachment length.Further increment of the Re and ER by 50% and 20% respectively, results in a 16% enhancement of the reattachment length.The turbulent flow, on the other hand, suggests turbulent intensities and turbulent kinetic energy are influenced primarily by Re.Overall, these results suggest important differences between closed-channel and open-channel BFS flows on the one hand, as well as narrow and wide open-channel BFS flows on the other hand.
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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 | 0.000 |
| 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 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".