A Numerical Investigation of Time Resolved Flows
Bibliographic record
Abstract
The complex nature of turbomachinery flows and the scale of associated flow phenomena such as shock waves and vortex shedding, apply constraints to the methods by which the flow can be analysed experimentally. Computational techniques have quite successfully been applied to the flow around turbine blades, but the transient and periodic phenomena observed in experimental studies have not been fully investigated. In this work an original working computational code is presented for time-resolved flows around turbine cascades. The code has been verified using test cases relevant to transonic flow. Some of the problems associated with computational techniques have been highlighted; these include the large number of schemes that are available, each with its own advantages and disadvantages. The code has been applied to a geometry representing highly loaded turbine blading currently under study at the National Research Council of Canada; this was also used extensively in previous computational and experimental investigations. The blading chosen has a relatively thick trailing edge, necessitated by cooling considerations. A distribution of the flow properties on the surface of the blade has been determined, from which an equivalent water table model has
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".