Experimental and Computational Analysis of a Multistage Axial Compressor Including Stall Prediction by Steady and Transient CFD Methods
Bibliographic record
Abstract
Siemens Energy has commissioned an extensive multiyear experimental and numerical (computational fluid dynamics (CFD)) project to improve its ability to design for and predict compressor stall. The experimental test rig is a half scale six stage axial compressor. The goal of this work is to provide insight into how best to predict the compressor performance map and in particular the stall point by applying state-of-the-art multiple blade row CFD simulation tools. A preliminary CFD analysis quantified numerical, model, and systematic error on the first stage of the compressor. Subsequent steady (mixing plane) and transient (time transformation) CFD simulations of the entire six stage compressor are compared to each other and to experimental data. Both the steady and transient simulations are shown to be computationally efficient and in very good agreement with the experimental data across the full performance map, up to stall inception on multiple speedlines. 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".