Bibliographic record
Abstract
To develop the turbocharger matching system, a compressor model and turbine model need to be developed independently and coupled. This paper describes the construction of the centrifugal compressor model and its validation with the experimental data. The compressor model in this paper uses One-dimensional (1D) thermo-fluid equations to analyse the compressor side of a turbocharger. Under a specified set of turbocharger geometry, atmospheric conditions, rotational speed, and fluid mass flow rate, the model can calculates the static and total temperatures, velocities, static and total pressures, pressure losses, and isentropic efficiencies for each compressor component. Instead of using lumped loss parameters, the compressor model includes established loss models found in the open literature. Not only in the impeller, the losses in the diffuser and the volute are modelled. With the model, it is possible for a parametric study on the effect of each loss mechanism on the performance, and which can aid the designer in justifying design decisions minimising the magnitude of the losses and thus positively influence the overall performance. The compressor model can also be applied to investigate the two stage Turbocharging or Variable Geometry Turbocharger (VGT) in the future.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".