Math-based spark ignition engine modelling including emission prediction for control applications
Bibliographic record
Abstract
A complete spark ignition (SI) engine model is a multi-domain model including fluid dynamics, thermodynamics, combustion, electrical, and mechanical sub-models. The complexity of these models depends on the type of analysis used for model development, which may vary from highly detailed computational fluid dynamics (CFD) analysis (multi-dimensional model) to simpler data-based analysis in which the data is obtained from experiments (zero-dimensional model). The main objective of our research is to develop a math-based SI engine model for control application and real time simulation. The model must be accurate enough to capture the combustion characteristics (e.g., combustion temperature) and predict emission gases, while being fast enough for real time simulation purposes. In this paper, a physics-based model of an SI engine is presented which consists of different sub-models including: throttle body and manifold model, four-stroke quasi-dimensional thermodynamic model of gas exchange and power cycles, two-zone combustion and flame propagation model, emission gases model based on the chemical kinetics equations, and mechanical torque model. Moreover, part of the simulation results is validated against the GT-Power simulation results. The math-based model is created in the MapleSim environment. The symbolic nature of MapleSim significantly shortens the simulation time and also enables parametric sensitivity analysis.
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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) | 0.000 | 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".