Down-speeding diesel engines with two-stage turbochargers: Analysis and control considerations
Bibliographic record
Abstract
Diesel engines continue to be used in truck applications, so reducing fuel use and hence CO 2 emissions, is a priority. Single-stage turbocharged diesel engines are known to be fuel efficient under steady load at low speeds. However, the engine’s ability to track load transients becomes limited by emission constraints due to the rate of production values for smoke and the resulting higher nitrogen oxides (NO x ). Modern air-path solutions including a variable geometry turbine (VGT) and high pressure exhaust gas recirculation (EGR) can be used to improve dynamic response without increasing NO x emissions, but lead to complex interactions that can be difficult to control. This paper develops a two-stage, in-series, air-path configuration, which improves the typical part-load performance at low engine-speeds through adjustments to the turbine expansion ratios. Better EGR rates (for NO x reduction) at low engine speeds can be achieved whilst the engine transient response is maintained. The air-path system is simulated using Ricardo Wave software and analysed for steady-state and transient behaviour in order to identify the relationships, constraints and performance measures for different operating regions that specify the controller requirements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".