Computational Fluid Dynamics Analysis of Self-Sustained Laminar Flow Oscillations in Singular Grooved Ducts
Bibliographic record
Abstract
Research Background: Modeling oscillatory flow in singular grooved ducts within the laminar regime provides insight into fluid mechanics that could enhance applications such as heat transfer in nuclear reactors. Previous research has identified pulsations in grooved channels under turbulent conditions with high Reynolds numbers, contributing to enhanced heat exchange efficiency. These pulsations are influenced by the axial flow in and out of the main channel from a continuous groove. Contribution of This Study: This study extends the understanding of flow oscillations to simpler geometries and lower Reynolds numbers, specifically within a laminar flow regime capped at a Reynolds number of 2,000. By simplifying the duct geometry to a rectangular main channel with a singular continuous groove, the research employs computational fluid dynamics tools, primarily OpenFOAM, facilitated by Compute Canada's clusters. It investigates the fluid flow characteristics—strength, frequency, velocity gradient, and oscillation behaviours—associated with the geometry of the gaps and channels. This approach helps in pinpointing the dependency of flow characteristics on specific geometric configurations, potentially laying groundwork for optimizing designs in practical applications.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".