Preliminary Design and Analysis Tool for Aeroengine Turbine Fixings
Bibliographic record
Abstract
A single platform D&A (Design & Analysis) tool is outlined in this paper that allows a user, through a single GUI (Graphical User Interface), to create a turbine rotor fixing as well as analyze the structural integrity of the fixing. This is done through the integration of CAD (Computer Aided Design) and FEA (Finite Element Analysis) software running in batch mode, driven by the GUI. This SPIE (Single Platform Integration Environment) captures the strength of CAD software to create a fully parameterized fixing that is able to model legacy, current designs and provides flexibility to design fixings not yet conceived. Using the automated use of FEA software through a secure and reliable gateway, stress analysis can be performed and the results displayed back to the user through the GUI. This tool provides a significant increase in quality and time savings to design a fixing when compared to the previous design methodologies. What used to take hours to design and analyze through the use of isolated specialist built and owned tools with little communication between them and non-ideal data management, now takes minutes; a reduction of up to 10 fold in the time taken.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".