Impact of depth-ratio on shear-layer dynamics and wake interactions around wall-mounted prisms
Bibliographic record
Abstract
This numerical investigation explores the flow dynamics around wall-mounted prisms with small aspect-ratio (AR=0.25−1.5) and changing depth-ratio (streamwise length, DR=1−4) at a Reynolds number of Re=1000−2500. This study focuses on understanding the formation and evolution of Kelvin–Helmholtz Instability (KHI) and its interactions with coherent wake structures, e.g., hairpin-like vortices. Additionally, it examines the influence of depth-ratio on prism surface pressure distribution and the origin of pressure fluctuations. The results, driven from the extreme geometrical cases of AR=1, DR=1 and 4 at Re=2500, reveal distinct KHI rollers originating from the leading edge shear layer. These impact prism surface pressure distribution and contribute to downstream wake structures. Interactions between KHI rollers and coherent wake structures are more pronounced for larger depth-ratio prisms, leading to a complex wake system. These interactions are quantified using turbulence–mean-shear interaction and turbulence–turbulence interaction from analyzing the Poisson equation. Cross-spectral density analysis highlights the influence of KHI rollers on coherent structures in the wake. These findings emphasize the significance of depth-ratio in shaping prism flow dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".