Development of a Fuel Injection Strategy for Diesel LTC
Bibliographic record
Abstract
A production V-8 engine was redesigned to run on low temperature combustion (LTC) with conventional Diesel fuel. Two fuel injection strategies were used to attain reduction in soot and NOx; a) early premixed injection strategy: fuel injected early during the compression stroke and b) late premixed injection strategy: fuel injected close to TDC with heavy EGR. The early premixed injection strategy yielded low NOx and soot but struggled to vaporize the fuel as noted in unburned hydrocarbons readings. The late premixed injection strategy introduced the fuel at higher in-cylinder temperatures and densities, improving the fuel's vaporization and limited the unburned hydrocarbon and carbon monoxide. The use of high EGR and high injection pressure for late premixed injection strategy provided sufficiently long ignition delay that resulted in partially premixed cylinder charge before combustion, and thereby prevented high soot, even in presence of high EGR. The engine operation was enhanced by a specially designed controller to balance all engine cylinders with the aide of in-cylinder pressure transducers. Under early premixed injection timings, where combustion phasing control was more challenging, the controller was responsible for reducing the soot in some cases by over 50%. The engine mapping was performed for a range of BMEP up to 7 bar. The CHEMKIN simulation of the combustion process reproduced the experimental results, specially the start of cool flame reactions for the early premixed injection strategy. The simulations provided a useful development tool to frame the boundary conditions towards optimum ignition timing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".