Tightened intake oxygen control for improving diesel low-temperature combustion
Bibliographic record
Abstract
The low-temperature combustion (LTC) operation in diesel engines is challenged by the higher cycle-to-cycle variation with heavy exhaust gas recirculation (EGR) and high unburnt hydrocarbon and carbon monoxide emissions. Small variations in the intake charge dilution can further decrease the combustion efficiency, which in turn escalates the successive fluctuations of the cylinder charge, adversely affecting the stability and efficiency of the LTC operation. In this work, improvements in the promptness and accuracy of combustion control as well as tightened control on the intake oxygen concentration have been combined to enhance the robustness and efficiency of the LTC operation in diesel engines. The empirical set-up consisted of a set of field programmable gate array (FPGA) modules that were coded and interlaced to execute on-the-fly combustion event modulations on either a cycle-by-cycle or within-the-same-cycle basis. The cylinder pressure traces were analysed to provide the necessary feedback for the combustion control algorithms. A methodology for estimating the indicated mean effective pressure for the current engine cycle 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 improved fuel efficiency and exhaust emissions. Moreover, a strategy for enabling load transients within narrow LTC operating corridors was implemented and shown to improve the load management of the LTC cycles. 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".