Efficient Time Resolved Multistage CFD Analysis Applied to Axial Compressors
Bibliographic record
Abstract
Unsteady computations are necessary if blade row interactions effects are relevant, for example for detailed optimization of a compressor at off-design conditions towards the aerodynamic stability limit, or for structural mechanical tuning of the blades. Modeling time accurate transient multistage flow is expensive both in terms of computer time and memory. Recently the implicit time-resolved Time Transformation method (based on Giles’ time inclining) has been shown to be computationally efficient and a good alternative for modeling transient flow in a single stage (one pitch ratio) turbomachinery configuration. A further advantage of this time resolved method is its ability to capture not only blade passing frequencies but also self-excited frequencies such as in wakes and tip vortex shedding. In this work, an extension of the Time Transformation method (TT) to multistage modeling has been employed to assess the method’s ability in predicting modern multistage compressor performance speedline curve, as well as its ability in capturing dominant machine frequencies. The multistage TT method is verified on a two and a half stage modified Hannover compressor, followed by an industrial validation on a Siemens Energy half scale six stage axial compressor based on the last stages of the Siemens Platform Compressor (PCO). Reference transient solutions on reduced portions of the compressor and/or modified blade count solutions are obtained and compared directly to single passage multistage Time Transformation predictions for the Hannover compressor. The method is then applied directly to the full six stage Siemens compressor employing the true blade counts for all six stages. The first goal of this work is to investigate the ability and accuracy of the multistage TT method to capture all relevant blades passing frequencies, including the impact of different degrees of pitch change between components. The second goal of this work is to explore how best to apply the method for the prediction of a compressor map, up to the surge line. Solutions are compared to experimental test rig data. Physical explanations of the key flow features observed in the experiment, as well as of the differences between the predictions and experimental data, are given.
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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) | 0.000 | 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) | 0.002 | 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".