Development and Validation of a Model for Centrifugal Compressors in Reversed Flow Regimes
Bibliographic record
Abstract
Abstract Turbochargers are widely used to help reduce the environmental impact of automotive engines. However, a limiting factor for turbochargers is compressor surge. Surge is an instability that induces pressure and flow oscillations that often damages the turbocharger and its installation. Most predictions of the surge limit are based on low-order models, such as the Moore–Greitzer model. These models tend to rely on a characteristic curve for the compressor created by extrapolating the constant speed lines of a steady-state compressor map into the negative mass flow region. However, there is little validation of these assumptions in the public literature. In this article, we develop further the first-principles model for a compressor characteristic presented in Powers, K., Brace, C., Budd, C., Copeland, C., & Milewski, P., 2020, “Modeling Axisymmetric Centrifugal Compressor Characteristics From First Principles,” J. Turbomachinery, 142(9), with a particular emphasis on reverse flow. We then perform experiments using a 58 mm diameter centrifugal compressor provided by Cummins Turbo Technologies, where we feed air in the reverse direction though the compressor while the impeller is spinning in the forward direction to obtain data in the negative mass flow region of the compressor map. This demonstrated experimentally that there is a stable operating region in the reverse flow regime. The recorded data showed a good match with the theoretical model developed in this article. We also identified a change in characteristic behavior as the impeller speed is increased, which, to the authors’ knowledge, has not been observed in any previously published experimental work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".