Finite element simulations of the thermomechanically coupled responses of thermal barrier coating systems using an unconditionally stable staggered approach
Bibliographic record
Abstract
Thermal barrier coating (TBC) systems have long been used in many engineering applications, such as jet engines and gas turbines. One pressing task is to evaluate their performance and integrity under long-term thermal cycles. This task requires an accurate and efficient time integration technique that is unconditionally stable, since large time steps are required to simulate TBC systems under long-term thermal effects. In this work, we present in detail several numerical methods to model the thermomechanically coupled responses of TBC systems, including a monolithic approach and two staggered approaches based on the isothermal split and the adiabatic split, respectively. We also demonstrate several finite element simulation techniques, such as the periodic boundary conditions and the adaptive mesh refinement, in the modeling of TBC systems. Through several numerical examples, we show that the staggered approach based on the adiabatic split preserves the unconditional stability of individual time integration techniques used for the mechanical phase and the thermal phase of the coupled problem. Moreover, the computational cost is significantly lower compared to the monolithic approach. This work provides an efficient time integration approach to model more complex behaviors of TBC systems under long-term thermal cycles. • Thermomechanically coupled responses of thermal barrier coating are investigated. • Time integration methods based on monolithic and staggered approaches are presented. • Staggered approach based on adiabatic split preserves unconditional stability. • Periodic boundary conditions and adaptive mesh refinement improve efficiency. • Stress concentration at interfaces inside thermal barrier coating is demonstrated.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".