Bibliographic record
Abstract
This dissertation deals with the computational study of free-surface flows with air entrainment. The aim of the study was to identify a suitable multiphase flow model that is capable of not only simulating the intricate flow physics but is also able to capture the free-surface deformations and predict the air entrainment at a reasonable computational cost. Finite volume based computations were performed using STAR-CCM+ commercial solver. The volume of fluid (VOF) multiphase model was used in the present study. First, a submerged hydraulic jump with an inlet Froude number F1 = 8.2 is simulated to determine the capabilities of the VOF multiphase model in capturing the free-surface deformations and other flow characteristics. The submerged hydraulic jump entrains lesser quantities of air and the free-surface deformations are not as abrupt as the classical hydraulic jump. Hence, this problem was chosen as a benchmark to validate the model. The VOF multiphase model was able to accurately capture the submerged hydraulic jump flow field. Proper orthogonal distribution (POD) analysis of the fluctuating velocity of the submerged hydraulic jump revealed the breakdown of large-scale structures into smaller-scale structures by the interaction of the roller and wall-jet flow, leading to the dissipation of energy.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".