Efficiency & Stability Improvements of Diesel Low Temperature Combustion through Tightened Intake Oxygen Control
Bibliographic record
Abstract
Diesel engines operating in the low-temperature combustion (LTC) mode generally tend to produce very low levels of NOx and soot. However, the implementation of LTC is challenged by the higher cycle-to-cycle variation with heavy EGR operation and the narrower operating corridors. Small variations in the intake charge dilution can significantly increase the unburnt hydrocarbon and carbon monoxide emissions as well as escalate the consecutive cyclic fluctuations of the cylinder charge. This in turn adversely affects the robustness and efficiency of the LTC operation. However, Improvements in the promptness and accuracy of combustion control as well as tightened control on the intake oxygen concentration can enhance the robustness and efficiency of the LTC operation in diesel engines. In this work, a set of field programmable gate array (FPGA) modules were coded and interlaced to suffice on-the-fly combustion event modulations on a cycle-by-cycle basis. The cylinder pressure traces were analyzed to provide the necessary feedback for the combustion control algorithms. The combustion phasing was estimated using a computationally-efficient Pressure Departure Ratio Algorithm that helped to anchor the combustion within a narrow crank angle window for the best efficiency. The load variations were minimized by regulating the indicated mean effective pressure that helped to stabilize the LTC cycles. Engine dynamometer tests demonstrated that such systematic and prompt control algorithms were effective to optimize the LTC cycles for better fuel efficiency and exhaust emissions. The reported techniques were in part to establish a model based control strategy for robust diesel LTC operations.
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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) | 0.001 | 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".