CFD-based Data-driven Modeling of Reactivity and Stratification Dynamics for RCCI Engine Control
Bibliographic record
Abstract
Reactivity Controlled Compression Ignition (RCCI) is a Low-Temperature Combustion (LTC) regime that provides thermal efficiency and emissions benefits compared to traditional Spark Ignition (SI) and Compression Ignition (CI) regimes. However, it is difficult to control combustion at these engines and run them at optimal conditions due to dependency of the their combustion on air-fuel mixture chemical reactivity and fuel stratification inside the combustion chamber. Modeling of reactivity and stratification can provide new pathways to control combustion in RCCI engines. In this study, a data-driven approach based on Computational Fluid Dynamics (CFD) results and a Linear Parameter Varying (LPV) method is proposed to model reactivity and stratification at RCCI engines. This work is illustrated for a real 2-liter 4-cylinder engine. The results show that the developed data-driven model (DDM) has acceptable prediction accuracy to estimate reactivity and stratification for RCCI engine control. The proposed method can be also implemented in other combustion modes in internal combustion engines.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".