Multidimensional simulation of diesel engine cold start with advanced physical submodels
Bibliographic record
Abstract
The complex physical processes occurring during cold starting of diesel engines mandate the use of advanced physical submodels in computations. The present study utilizes a continuous probability density function to represent more fully the range of compositions of commercial fuels. The model was applied to singledroplet calculations to validate the predictions against experimental results. Analysis of a high-pressure diesel spray showed axial composition gradients within the spray. Previous wall-film modelling was extended to include the continuous multicomponent fuel representation. Using these models, the cold-start behaviour of a heavy-duty diesel engine was analysed. The predictions show that multicomponent fuel modelling is critical to capturing realistic vaporization trends. In addition, the spray-film interaction modelling is crucial to capturing the spray impingement and subsequent secondary atomization. Heating the intake air temperature was shown to result in reduced ignition delay and accelerated vaporization. Increasing the fuel temperature increased vaporization prior to and away from the initial heat release. Increasing the injection pressure increased vaporization without much change in the ignition delay. Split injections, with 75 per cent of the fuel contained in the second pulse, displayed a substantial reduction in ignition delay due to ignition of the first pulse. The timing of the first injection was found to be an important parameter due to differences in the spray impingement behaviour with different timings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".