Experimental and Numerical Characterization of Transonic Compressor Subjected to Inlet Distortion
Bibliographic record
Abstract
In this work we show preliminary results of an experimental and numerical investigation of a transonic axial compressor subjected to a 90 degree circumferential inlet total pressure distortion. The fundamental goal of the investigation is to establish an experimental dataset to be used in the validation of numerical simulation tools. The special interest of this investigation is the ability of the advanced blade row tools such as harmonic balance to reproduce the experimental results. Therefore, this report is account of the initial work of the ongoing project of the validation of advanced blade row tools in ANSYS CFX. Measurements of the compressor’s performance and efficiency were obtained by “traversing” the distortion around the test article inlet over 24 separate experiments. The resultant 2-D total pressure and total temperature contours at three measurement stations are shown. The experimental instrumentation package also included high sampling frequency rotor casing static pressure measurements and high sampling frequency rotor exit total pressure measurements. Phase locked averaged casing static pressure and rotor exit total pressure at four locations relative to the inlet distortion are shown. The initial set of computations presented in this paper concentrate on a full annulus transient simulation conducted in preparation for the harmonic analysis simulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".