Heat Release Pattern Diagnostics to Improve Diesel Low Temperature Combustion
Bibliographic record
Abstract
Empirical results indicated that the engine emission and fuel efficiency of low-temperature combustion (LTC) cycles can be optimized by adjusting the fuel-injection scheduling in order to obtain appropriate combustion energy release or heat-release rate patterns. Based on these empirical results the heat-release characteristics were correlated with the regulated emissions such as soot, hydrocarbon and oxides of nitrogen. The transition from conventional combustion to LTC with the desired set of heat-release rate has been implemented. This transition was facilitated with the simplified heat-release characterization wherein each of the consecutive engine cycles was analyzed with a real-time controller embedded with an FPGA (field programmable gate array) device. The analyzed results served as the primary feedback control signals to adjust fuel injection scheduling. The experimental efforts included the boost/backpressure, exhaust gas recirculation, and load transients in the LTC region. The impact of heat-release phasing, duration, splitting, and shaping on the combustion-efficiency and cylinder pressure-rise rate was also analyzed with an engine cycle simulation to facilitate the model-based control.
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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 | 0.000 |
| 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".