DE1-2: Adaptive Control to Improve Low Temperature Diesel Engine Combustion(DE: Diesel Engine Combustion,General Session Papers)
Bibliographic record
Abstract
The fuel efficiency of the low temperature combustion (LTC) cycles is commonly compromised by the high levels of hydrocarbon (HC) and carbon monoxide (CO) emissions. More seriously, the scheduling of fuel delivery in HCCI engines has lesser leverage on the exact timing of auto-ignition that may even occur before the compression stroke completes, which may cause excessive efficiency reduction and combustion roughness. New LTC control strategies have been explored experimentally to achieve ultra low emissions under independently controlled EGR, intake boost, exhaust backpressure, and multi-event fuel injection with up to 12 fuel injection pulses per cycle for conventional diesel and neat bio-diesel fuels. Adaptive control strategies based on cylinder pressure characteristics have been implemented to enable and stabilize the LTC when heavy EGR is applied. The impact of heat release phasing, duration, shaping, and splitting on the thermal efficiency has been analyzed with engine cycle simulations. Oxygen sensors at the intake and exhaust of the engine are devised to comprehend the transient impacts of EGR, boost, and load variations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".