A Method to Determine Fuel Transport Dynamics Model Parameters in Port Fuel Injected Gasoline Engines During Cold Start and Warm-Up Conditions
Bibliographic record
Abstract
In order to meet stringent emission standards, it is essential to have a precise control of air-fuel ratio (AFR) under cold start and warm-up conditions. This requires an understanding of the fuel transport dynamics in the intake system during these conditions. This study centers on estimating the parameters of a fuel transport dynamics model during engine operation at different thermal conditions ranging from cold start to fully warmed-up conditions. A method of system identification based on perturbing fuel injection rate is used to find fuel dynamics parameters in a port fuel injected (PFI) spark ignition engine. Since there was no cold chamber available to prepare cold start conditions, a new method was utilized to simulate cold start conditions. The new method can be applied on PFI engines, which use closed valve injection timing. A four-cylinder PFI engine is tested for different thermal conditions from −15°C to 82°C at a range of engine speeds and intake manifold pressures. A good agreement is observed between simulated and experimental AFR for 52 different transient operating conditions presented in this study. Results indicate that both fuel film deposit factor (X) and fuel film evaporation time constant (τf) decrease with increasing coolant temperature or engine speed. In addition, an increase in the intake manifold pressure results in an increase in X while causes a decrease in τf.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".