Modeling Axisymmetric Centrifugal Compressor Characteristics From First Principles
Bibliographic record
Abstract
Abstract Turbochargers are a vital component for aiding engine manufacturers in meeting the latest emissions standards. However, their range of operation is limited for low mass flows by compressor surge. Operation in surge results in pressure and mass flow oscillations that are often damaging to the compressor and its installation. Since surge is a highly complex flow regime, full unsteady three-dimensional models are generally too computationally expensive to run. The majority of current low-dimensional surge models use a cubic compressor characteristic that needs to be fitted to experimental data. Therefore, each time a compressor is studied using these models, costly experimental testing is required. In this paper, a new technique for obtaining an axisymmetric centrifugal compressor characteristic is presented. This characteristic is built using the equations of mass, momentum, and energy from first principles in order to provide a more complete model than those currently obtained via experimental data. This approach enables us to explain the resulting cubic-like shape of the characteristic and hence to identify impeller inlet stall as a route into surge. The characteristic is used within a quasi-steady, map-based surge model in order to demonstrate its ability to predict the onset of surge while only providing geometric data as input. Validation is provided for this model by a discussion of the qualitative flow dynamics and a good fit to experimental data, especially for low impeller speeds and pressure ratios.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".