Failure and damage evaluation of thermal barrier coatings under thermal cyclic environments: multi-physics modeling
Bibliographic record
Abstract
Abstract Under high-temperature adverse environments, the premature failure of air plasma spray thermal barrier coatings (APS-TBCs) is a preliminary phenomenon that can significantly limit the application of TBCs in gas turbine engines. The delamination failure of TBCs typically occurs at the interfaces between the topcoat and bond coat due to thermal mismatch stress and thermal gradient, resulting in crack propagation and final spallation failure of the coating. This paper undertakes a study of the delamination of TBCs using multi-physics methodologies. Heat transfer was cyclically implemented into the TBC model, resulting in a thermal gradient, to simulate the in-service operation of the TBC system. A variational-based sintering model for a topcoat of TBCs is incorporated into the simulation. The high-temperature creep model of the topcoat, thermal growth oxide (TGO) and bond coat is included. The stress field across the TBCs was calculated during the thermal cycles, with the location of high-stress concentration selected as the potential crack initiation site. Phase field damage modelling was conducted to study crack propagation, initially located at the high-tensile stress off-peak interface. Results indicate that at the off-peak interface, the crack propagates rapidly in the top right direction during the first few cycles, then stops propagating as the TGO thickens, because the location of the maximum principal stress is moved away from the interface. Since the accumulated stress at the crack tip at the end of cycles, cracks have the potential to propagate with a prolonged thermal cycle service.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".