Finite Element Modeling of Shot Peening Residual Stress Relaxation in Turbine Disk Assemblies
Bibliographic record
Abstract
Surface enhancement techniques such as shot peening are extensively used to increase the fatigue life of components in gas turbine engines. Due to the combined thermomechanical nature of the loading encountered within an engine, aeroengine designers have avoided incorporating the beneficial effects in their analysis. This can lead to overdesign and early retirement of critical engine components. A finite element modeling procedure is introduced that incorporates the shot peening residual stresses on a fir-tree turbine disk assembly. Unlike traditional equivalent loading approaches, the method models the actual impact of shots on the assembly and is the first time this approach is used to introduce peening residual stresses in turbine disks. In addition, the stability of these residual stresses in response to cyclic thermomechanical loadings at the contact interface is also studied. The results reveal that thermomechanical overload can nearly fully relax the shot peening residual stresses within the first cycle due to the combined effects of decreased material yield strength and plastic deformation. This work will enable aeroengine designers to assess critical surface treated components for structural integrity, optimal design, and residual life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".