Insights Into Turbocharger Centrifugal Compressor Stability Using High-Fidelity Computational Fluid Dynamics and a Novel Impeller Diffusion Factor
Bibliographic record
Abstract
Abstract Turbocharger technologies are widely applied in the modern automotive industry to meet increasingly stringent carbon emission standards. To support the continuous enhancement of these tactics, delivering improvement to the performance and stability of turbocharger compressors at low mass flowrate regimes is of paramount importance. This chapter investigates the stability of a turbocharger compressor stage equipped with a ported shroud, using high-fidelity computational fluid dynamics (CFD) simulations. It was found that the recirculated flow passing through the ported shroud device had primary influence on the flow condition in the vicinity of the impeller tip region (80–100% span). This discovery facilitated the definition of a modified impeller diffusion factor (DF), which was applied to the CFD results for three different compressor geometry configurations to lead to the provision of quantitative metrics for impeller stability. Furthermore, the observed change in the limiting impeller DF along the compressor surge line indicated that the dominant components governing the instability of the entire stage had switched, i.e., the stalling behavior of the compressor stage had transitioned from impeller dominated stall to vaneless diffuser dominated stall. Additionally, the limiting impeller DF clearly showed that one of the compressor configurations with a change in the hub endwall recess around the rear of the impeller improved the impeller stability by 3%. By comparison, another configuration with a change to strut geometry in the ported shroud recirculation channel reduced the impeller stability by 10%. The present work addressed the gap in the understanding about the stability of different compressor configurations equipped with a ported shroud, and was useful for determining the changes in compressor global and local instability through a quantitative parameter based on a modified definition of DF.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".