Generic Framework for an Accurate, and Efficient Flutter Analysis
Bibliographic record
Abstract
Abstract Accurate and efficient prediction of blade aerodynamic damping is critical for the design and durability of turbomachines such as gas and steam turbines. Several empirical and linearized methods have been developed and used with varied degrees of success over the past decade, especially for bladed components. This paper presents a generic framework to carry out flutter analysis which is equally applicable for bladed turbomachinery components as well as other complex components in the engines such as seals, and valves. A streamlined workflow to carry modal analysis and then perform efficient CFD analysis using one or multiple modes to obtain accurate aerodamping is outlined. A recent implementation of phase-lag approach with traveling wave method in Ansys Fluent is described. This approach enables calculation of flutter for any nodal diameter with just a single sector of the turbomachinery. Furthermore, the implementation of Aerodynamic Influence Coefficient method for a very fast turnaround time to get aerodamping for a range of nodal diameters is discussed. The framework presented is validated and presented for two cases. The first testcase is Rotor 1 from the 4.5 stage high-speed Hannover compressor, which represents standard bladed turbomachinery. A range of nodal diameters are investigated using both the travelling wave method and influence coefficient method. The accuracy of phase lag approach is compared with the reference full-wheel solution. The speedup provided by the phase lag approach and influence coefficient method is discussed in detail. The second testcase is a generic gas turbine seal representing a complex feature in modern turbomachinery. It is shown that the framework presented and validated for bladed geometry can be used without any further modifications for such a complex case.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".