3D Crack Growth Analysis and Its Correlation With Experiments for Critical Turbine Components Under an International Collaborative Program
Bibliographic record
Abstract
Following a reanalysis of the original material data plus supplementary Low Cycle Fatigue (LCF) specimen testing, an Original Equipment Manufacturer (OEM) reduced the low cycle fatigue life limits for a number of turbine components. To ascertain the validity of the new life limits, an international collaborative spin rig test program was initiated to provide more accurate low cycle fatigue life limits. The program covered a broad range of activities including, Finite Element (FE) stress analyses, cyclic spin rig testing, fractographic assessment and fatigue crack growth (FCG) analyses. This paper describes the 2D and 3D crack growth analyses of critical turbine components in a turboprop gas turbine engine, comparison of predicted results obtained using different software and also correlations with spin test results from the program. First, FE stress analyses of selected turbine components were carried out under both engine operating conditions and spin-rig test configurations in order to determine the maximum and minimum operating speeds required to match the stress ranges at the critical location specified by the OEM under engine operating conditions. Second, 2D and 3D crack growth analyses were performed independently by three organisations for a disk bolthole using the state-of-the-art software. Third, the predictions from different software were compared, and the relative technical merits of each software were evaluated. Finally, the predicted results were correlated against the striation counts determined by the OEM from the results of spin rig tests.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".