Sintering Modeling of Thermal Barrier Coatings at Elevated Temperatures: A Review of Recent Advances
Bibliographic record
Abstract
To achieve a higher efficiency in gas turbine engine by increasing the inlet-temperature of burning gas is one of the primary goals in aviation industry. The development of thermal barrier coating system (TBCs) continuously raises the inlet-temperature of gas turbine engine in the past decades. Due to the complexity of TBCs and harsh operation environments, the degradation and failure mechanisms of hot section components have not been fully understood, and consequently limits the application of TBCs. It was identified that high-temperature sintering of the topcoat in a typical TBC could be one of the major sources of its failure since the microstructures of the constituent coating layers evolve dynamically during the service period, resulting in significant changes of mechanical and thermal physical properties of the coating system. This paper intends to review recent advances of analytical and numerical modeling of sintering of topcoat in TBCs including the modeling methodology and applications of the models, particularly the implementation of finite element combined with specific materials constitutive functions. Critical remarks on the future development and applications of these models are also discussed in the end.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".