An Investigation on the Effect of Creep Shakedown on the Creep Behavior of an Industrial Gas Turbine Blade
Bibliographic record
Abstract
The creep life computation of gas turbine hot section components using any damage modeling technique requires typical inputs of stress and temperature under actual engine operating conditions. The magnitude of these inputs is governed by the static or dynamic transient loading conditions that a component may be subjected to during service. The long term creep damage accumulation process in a hot section component leads to strain accumulation in the component with time. The rate of change of this strain accumulation in different regions of a component is controlled by the magnitude of the local stress, stress gradient and temperature. In some regions, the creep damage accumulation process may lead to a substantial change in the local stress distribution, also called the “Creep Shakedown”, and this time-dependent stress redistribution can have a substantial impact on the component creep life. The creep shakedown based creep life analysis of a GE Frame 7EA first stage turbine blade under off-design base-load engine operating conditions is studied. The evolution of the stress and strain in different regions of the blade with service time was analyzed using the finite element method. A user defined Garofalo model with hyperbolic sine creep rule was incorporated in the finite element analysis (FEA). The creep shakedown in the component is demonstrated to cause a local time-dependent stress redistribution effect in the FEA simulation. The significant stress variation and creep strain accumulation was observed in the creep critical regions where local stress raisers were present and/or a high temperature gradient due to internal cooling design existed. These effects are discussed in detail from a materials engineering perspective.
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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 | 0.001 | 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 | 0.000 |
| 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".