Thermomechanical Component Mode Synthesis for Blade Casing Interaction Prediction
Bibliographic record
Abstract
Increasing the efficiency of turbomachines is a major concern as it directly translates into lower environmental impact and improved operational costs. One solution is to reduce the blade-casing operating clearance in order to mitigate aerodynamic losses at the unavoidable cost of increased structural unilateral contact and friction occurrences. In centrifugal compressors, the dynamic behaviour of the structures interacting through unilateral contact and friction is not yet fully understood. In fact, the heat generated during such events may affect the dynamics through thermal stresses. This paper presents a complete thermomechanical modelling strategy of impeller rotor and casing, and of blade-tip/casing contact events. A fully coupled thermomechanical modal synthesis technique is introduced and applied to turbomachinery-related models. The blisk is reduced via a hybrid modal synthesis technique combining the Craig-Bampton method and the characteristic constraint mode method. The casing model is reduced using an axisymmetric harmonic modal synthesis. Both strategies involve thermomechanical modes embedding thermal dilatation effects. The contact modelling algorithm is then introduced. It handles unilateral contact and friction occurrences together with heating effects. This algorithm uses the above mentioned reduced-order models as input data to avoid CPU-intensive simulations. The results show that the thermomechanical behaviour of the structures is well preserved by the reduction strategy proposed. Contact simulations on simple cases show qualitative results in accordance with expectations. Further work is needed in order to validate the strategy based on experimental results. However, this methodology opens the way to extended multiphysics simulations of contact events in turbomachinery.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".