Investigation of Transient CFD Methods Applied to a Transonic Compressor Stage
Bibliographic record
Abstract
Understanding unsteady flow phenomena in compressor stages often requires the use of time-accurate CFD simulations. Due to the inherent differences in blade pitch between adjacent blade rows, the flow conditions at any given instant in adjacent blade rows differ. Simplified computation of the stage represented by a single blade in each row and simple periodic boundary conditions is therefore not possible. Depending on the blade counts, it may be necessary to model the entire annulus of the stage; however, this requires considerable computational time and memory resources. Several methods for modeling the transient flow in turbomachinery stages which require a minimal number of blade passages per row, and therefore reduced computational demands, have been presented in the literature. Recently, some of these methods have become available in commercial CFD solvers. This paper provides a brief description of the methods used, and how they are applied to a transonic compressor stage. The methods are evaluated and compared in terms of computational efficiency and storage requirements, and comparison is made to steady stage simulations. Comparisons to overall performance data and two-dimensional LDV measurements are used to assess the predictive capabilities of the methods. Computed flow features are examined, and compared with reported measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".