Flow characteristics of three-dimensional offset jet over surface-mounted ribs using large-eddy simulation
Bibliographic record
Abstract
This study employs large-eddy simulations to investigate the turbulent structure and wake dynamics of a three-dimensional offset jet interacting with surface-mounted ribs at width-to-height ratios W/H = 1, 2, and 3. First, the simulated results for W/H = 1 are benchmarked against a prior experimental study, demonstrating strong agreement in predicting the maximum local velocity profile and Reynolds normal stress within the recirculation region. Building on this validation, a comprehensive analysis is performed to describe the influence of rib geometry on flow characteristics, specifically within recirculation, reattachment, redevelopment, and downstream regions. Qualitative assessments, including mean velocity and turbulent kinetic energy contours, pressure coefficient fields, and streamline visualizations, are combined with quantitative evaluations of first- and second-order turbulence statistics (e.g., mean flow, root mean square velocities, and Reynolds shear stresses) to provide insights of the flow. The results reveal that flow separation begins at the leading edge of the ribs, and the region of elevated turbulence expands as the W/H ratio increases. The reattachment process and the evolution of the shear layer are strongly influenced by rib geometry, creating pronounced differences in turbulence production and transport. Furthermore, insight is obtained through single and joint probability density functions, two-point correlation analyses, and spectral density at three downstream locations. These techniques highlight how the largest rib induces an extended recirculation region and sustains turbulence energy farther into the wake. Taken together, these results demonstrate the significant influence of the width-to-height ratio on offset jet flow and wake dynamics, offering valuable insights into the complex interplay between turbulent flows and surface-mounted ribs.
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 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 | 0.000 |
| 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 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".