Rapid Airfoil Design for Uncooled High Pressure Turbine Blades
Bibliographic record
Abstract
The aero-engine design process is highly iterative, multidisciplinary in nature and complex. The success of any engine design depends on best exploiting and considering the interactions among the numerous traditional engineering disciplines such as aerodynamics and structures. More emphasis has been placed lately on system integration, cross disciplines leveraging of tools and multi-disciplinary-optimization at the preliminary design phase. This paper investigates the automation of the airfoil generation process, referred to as Rapid Airfoil 3D (RAF-3D), for uncooled high pressure turbine blades at the preliminary design phase. This approach uses the TAML (Turbine Aero Mean Line) program in parallel with a database of previously designed P&WC airfoils, in-house design rules and best practices to define a pre-detailed airfoil shape which can be fed back to other analytical groups for pre-detail analyses, such as for structural integrity and vibrations. Resulting airfoil shapes have been aerodynamically validated using an in-house three dimensional Reynolds averaged Navier-Stokes code. RAF-3D will shorten the turnaround time for Pratt & Whitney turbine aerodynamics group to provide a preliminary 3D airfoil shape to turbine structures group by up to a factor of ten. Additionally, the preliminary assessments of stress and vibration specialists will be more accurate as their assessments will be based on an airfoil that has had inputs from all functional groups even though it is "first pass" design.
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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 | 0.001 |
| 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".