Study of Cylinder Charge Control for Enabling Low Temperature Combustion in Diesel Engines
Bibliographic record
Abstract
Suitable cylinder charge preparation is deemed critical for the attainment of a highly homogeneous, diluted, and lean cylinder charge, which is shown to lower the flame temperature. The resultant low temperature combustion (LTC) can simultaneously reduce the NOx and soot emissions from diesel engines. This requires sophisticated coordination of multiple control systems for controlling the intake boost, exhaust gas recirculation (EGR), and fueling events. Additionally, the cylinder charge modulation becomes more complicated in the novel combustion concepts that apply port injection of low reactivity alcohol fuels to replace the diesel fuel partially or entirely. In this work, experiments have been conducted on a single cylinder research engine with diesel and ethanol fuels. The test platform is capable of independently controlling the intake boost, EGR rates, and fueling events. Effects of these control variables are evaluated with diesel direct injection and a combination of diesel direct injection and ethanol port injection. Data analyses are performed to establish the control requirements for stable operation at different engine load levels with the use of one or two fuels. The sensitivity of the combustion modes is thereby analyzed with regard to the boost, EGR, fuel types, and fueling strategies. Zero-dimensional cycle simulations have been conducted in parallel with the experiments to evaluate the operating requirements and operation zones of the LTC combustion modes. Correlations are generated between air–fuel ratio (λ), EGR rate, boost level, in-cylinder oxygen concentration, and load level using the experimental data and simulation results. Development of a real-time boost-EGR set-point determination to sustain the LTC mode at the varying engine load levels and fueling strategies is proposed.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".