Acausal powertrain modelling with cycle-by-cycle spark ignition engine model
Bibliographic record
Abstract
Due to more stringent emission limits and demand to improve fuel consumption, automotive researchers have been trying to develop detailed physics-based powertrain models for fuel consumption and emission control purposes. This paper presents an acausal powertrain model including vehicle longitudinal dynamics. The developed cycle-by-cycle spark ignition (SI) engine model is connected to a physics-based dynamic torque converter model, and the torque converter is connected to the rest of the powertrain (gear box, differential, tyres and chassis model) through acausal mechanical port connections. The SI engine speed and load are variable during the many-cycle powertrain simulation. The cycle-by-cycle four-stroke engine is based on a two-zone combustion modelling approach which assumes the engine speed is kept constant during a cycle. The SI engine and dynamic torque converter models are cross-validated with the GT-Power software and experimental data. The developed powertrain model in MapleSim is a suitable plant model that can be used for control applications due to the fast simulation time (faster than real time).
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".