Predicting HCCI Auto-Ignition Timing by Extending a Modified Knock-Integral Method
Bibliographic record
Abstract
One major challenge in Homogeneous Charge Compression Ignition (HCCI) combustion is the difficulty in controlling the timing of auto-ignition which is dependant on mixture conditions. Understanding the effect of modifying the properties of the engine charge on the start of combustion is essential to be able to predict and control the auto-ignition timing. The purpose of this work is to develop a realtime model for predicting HCCI auto-ignition timing. The standard Livengood and Wu Knock-Integral Method (KIM) is modified to work with values that are easier to measure compared with the instantaneous in-cylinder parameters required in the original KIM. This modified Knock-Integral Method (MKIM) is developed and is then parameterized using HCCI Thermokinetic Kinetic Model (TKM) simulations for a single cylinder engine. Estimating the MKIM parameters is done using an off-line optimization technique. Once the parameters have been identified, the MKIM needs only the rate of Exhaust Gas Recirculated (EGR), equivalence ratio, intake manifold temperature and intake manifold pressure to predict auto-ignition timing. The MKIM is validated with the experimental data from the single cylinder engine in HCCI operation by varying equivalence ratio, EGR level, engine speed, and intake temperature for three different blends of Primary Reference Fuels (PRF) at octane values of 0, 10 and 20.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".