Time Transformation Simulation of 1.5 Stage Transonic Compressor
Bibliographic record
Abstract
The accurate prediction of the aerodynamic and aeromechanical performance in a modern transonic compressor often exceeds the capability of traditional steady state mixing plane simulation methods. Time accurate transient blade row simulation approaches are required when there is a close coupling of the flow between the blade rows, and for fundamentally transient flow phenomena such as aeromechanical analysis including blade flutter and forced response, aerothermodynamic analysis and aero-acoustic analysis. Transient blade row simulations can be computationally impractical when all of the blade passages must be modeled to account for the unequal pitch between the blade rows. Most turbomachines consist of multiple stages, further exacerbating the computational challenge. In order to reduce the computational cost, time accurate pitch-change methods are utilized so that only a sector of the turbomachine (one or few passages per row) is modeled. The extension of the time-transformation pitch-change method to multistage machines has recently shown good promise in predicting both aerodynamic performance and resolving dominant blade passing frequencies for a subsonic compressor, while keeping the computational cost affordable. In this work, a modified one and a half stage Purdue transonic compressor (modified for unequal pitch for all three blade rows) is examined. The goal is to assess the ability of the multistage time-transformation method to accurately predict the aerodynamic performance and transient flow details in the presence of transonic blade row interactions. The results from the multistage time-transformation simulation are compared in detail with a transient full-wheel simulation, a profile transformation simulation, as well as to a steady-state mixing-plane model. Flow details are examined including an FFT analysis of select signals, and the onset of stall is compared between all methods. The relative computational effort is compared between all of the analysis methods.
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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".