Numerical investigation of heat transfer on film cooling with shaped holes
Bibliographic record
Abstract
Purpose To develop a reliable methodology and procedure of simulating the jet‐in‐crossflow using the current turbulence models and numerically investigate the cooling performance of a new scheme for the engines of next generation. Design/methodology/approach A new advanced film cooling scheme is proposed based on the literature survey and a systematic methodology developed to successfully predict the right level of heat transfer in the CFD simulation of film cooling. Findings The proposed cooling scheme gives considerable lower heat transfer coefficient at the centerline in the near hole region than the traditional cylindrical hole, especially at a high blowing ratio when traditional cylindrical hole undergoes liftoff. Research limitations/implications The number of cooling holes in the computational domain is limited by the speed of the computers used. Practical implications The new methodology can be used to numerically test new cooling schemes in the design of turbine blades and to provide useful information/data under actual working conditions to design engineers. Originality/value This paper provides some useful information on the simulation of film cooling in terms of the performance of different turbulence models and wall treatments and also sends some valuable messages regarding the design of cooling scheme of turbine blades to the technical community.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".