Low Temperature Combustion Strategies for Compression Ignition Engines: Operability limits and Challenges
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Low temperature combustion (LTC) strategies such as homogeneous charge compression ignition (HCCI), smokeless rich combustion, and reactivity controlled compression ignition (RCCI) provide for cleaner combustion with ultra-low NOx and soot emissions from compression-ignition engines. However, these strategies vary significantly in their implementation requirements, combustion characteristics, operability limits as well as sensitivity to boundary conditions such as exhaust gas recirculation (EGR) and intake temperature. In this work, a detailed analysis of the aforementioned LTC strategies has been carried out on a high-compression ratio, single-cylinder diesel engine. The effects of intake boost, EGR quantity/temperature, engine speed, injection scheduling and injection pressure on the operability limits have been empirically determined and correlated with the combustion stability and performance metrics. For dual-fuel combustion of diesel-ethanol (RCCI), the pilot-to-main fuelling ratio and pilot timing/quantity variations have been investigated to identify high-efficiency or high-load operation. The factors affecting the real-world application of these LTC strategies have been identified, the challenges needed to overcome have been highlighted and their effects on the engine performance quantified.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".