Predicting Start of Combustion Using a Modified Knock Integral Method for an HCCI Engine
Bibliographic record
Abstract
Homogeneous Charge Compression Ignition (HCCI) is a promising combustion concept for internal combustion engines to reduce emissions and fuel consumption. Unlike spark ignition and diesel engines in which ignition is controlled by spark and spray injection timing respectively, HCCI combustion auto-ignites given the correct mixture conditions which makes HCCI ignition difficult to control. It is thus critical to understand the characteristics of HCCI ignition timing in order to find suitable strategies for ignition control. This paper presents a modified model of ignition timing which is based on the Knock-Integral Method. Since this model doesn't require instantaneous in-cylinder parameters, it is suitable for control application on HCCI combustion. The model is tested using both simulation results of a Thermo-Kinetic Model and experimental data. With seven model parameters, the ignition timing of over 250 HCCI points at different conditions for four different Primary Reference Fuels (PRF) is predicted to within an average error of less than 1.5 degrees of crank angle. This model is computationally efficient and could be implemented in the engine control unit of an HCCI engine to calculate the required inputs that are needed to get the desired ignition timing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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".