System Identification of Multistage Turbine Engine Rotors
Bibliographic record
Abstract
Recently, an efficient approach for modeling the vibration of multistage rotors was developed by the authors [1, 2]. This reduced-order modeling technique employs component mode synthesis, with each stage (bladed disk) treated as a separate component. In addition, the component mode mistuning (CMM) projection technique was extended to multistage systems. In the CMM method, individual blade mistuning is transformed from a basis of cantilevered blade modes to the basis of tuned system modes used for the reduced-order model. In this paper, the component-based modeling framework developed for mistuned multistage turbine engine rotors is utilized for system identification. First, the identification of multistage mode types is considered. Strain energy ratios are used to identify which system modes are confined to mostly one stage and which modes show strong coupling among multiple stages. Simple approximations for these ratios are derived based on data from the component-level free response analysis that are performed during the model construction process. The component-level results are also utilized to identify the dominant nodal diameter number for each multistage mode, even though the multistage system does not possess cyclic symmetry because the stages have different numbers of blades. Second, the modes are further classified as to how much the blades participate in the response relative to the disk for each stage. As a systematic identification procedure, this is applicable to single-stage models as well. For multistage systems, this is used to determine operating conditions where coupled response among blades on adjacent stages is most likely to occur. Third, the application of mistuning identification techniques to multistage systems is considered. It is found that the proposed modal classification methods allow the determination of conditions under which deviations in individual blade properties may be observed indirectly from measurements of the disks and spacer.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".