Development of an optimized chemical kinetic mechanism for homogeneous charge compression ignition combustion of a fuel blend of <i>n</i> -heptane and natural gas using a genetic algorithm
Bibliographic record
Abstract
Homogeneous charge compression ignition (HCCI) is a promising technique for advanced low-temperature combustion strategies that offers a high fuel conversion efficiency and low nitrogen oxide and soot emissions. One of the major problems associated with HCCI combustion engine application is the lack of direct control for combustion timing. A proposed solution for combustion timing control is to use a binary fuel blend in which two fuels with different autoignition characteristics are blended at various ratios on a cycle-by-cycle basis. Because dual-fuel diesel—natural-gas engines have already been used, a fuel blend of n-heptane (diesel-like fuel) and natural gas (mostly methane) is one of the best available options. The objective of this study is to optimize the chemical kinetic mechanisms available for n-heptane and natural gas to be used in a binary-fuel blend scenario. Using the genetic algorithm method, a combined mechanism was optimized and modelling results were verified against experimental results. The agreement between experimental and modelling results was found to be acceptable within the examined conditions. As a result, an optimized chemical kinetic mechanism for an n-heptane—natural-gas blend is presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".