Preliminary design for turbine housing and shroud segments
Bibliographic record
Abstract
ABSTRACT At the preliminary design phase of a gas turbine, time is crucial in capturing new business opportunities. In order to minimise the design time, the concept of Preliminary Multi-Disciplinary Optimisation (PMDO) was used to create parametric models, geometry and cooling flow correlations towards a new design process for turbine housing and shroud segments. First, dedicated parametric models were created because of their reusability and versatility. Their ease of use compared to non-parameterised models allows more design iterations and reduces set-up and design time. A user interface was developed to interact with the parametric models and improve the design time. Second, geometry correlations were created to minimise the number of parameters used in turbine housing and shroud segment design. Third, a correlation study was conducted to minimise the number of engine parameters required in cooling flow predictions. The parametric models, the geometry correlations, and the user interface resulted in a time saving of 50% and an increase in accuracy of 56% compared to the existing design system. For the cooling flow correlations, the number of engine parameters was reduced by a factor of 6 to create a simplified prediction model and hence a faster shroud segment selection process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".