Instantaneous On-Engine Turbine Efficiency for an SI Engine in the Closed Waste Gate Region for 2 Different Turbochargers
Bibliographic record
Abstract
1D engine simulations of turbocharged engines are difficult to perform with good accuracy. Calculations of turbine performance are based on performance maps. These are measured under steady flow conditions using air at moderate temperatures, not very representative of the very hot and pulsating gas flow the on-engine turbine is exposed to. To improve the predictivity of today's 1D engine calculations or the limiting factors of the turbocharger itself, it is most important to gain deeper understanding of how the turbine behaves under on-engine conditions. The objective of this paper is to compare calculated instantaneous on-engine turbine efficiency based on measurements with results from using steady-flow efficiency performance maps. The work is performed using two different turbochargers at two operating points with closed waste gate. It is shown that the turbine efficiency characteristic derived from measurements and that from using steady-flow efficiency performance maps describe a quite different behavior of the turbine. The on-engine turbine efficiency has systematically shown to be asymmetric over an exhaust pulse. It is considerably higher during the “downhill side” of the pulse, a phenomenon not captured by the 1D quasi steady calculations. An error estimation is made for the measurement-based efficiency. The cumulative error results from individual measurement errors of its constituent parameters. The efficiency uncertainty is most governed and very sensitive to the measurement error of the turbine shaft speed. The pressure before and after the turbine are also important to measure correctly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".