Investigating cylinder deactivation as a low fuel-penalty thermal management strategy for heavy-duty diesel engines
Bibliographic record
Abstract
The upcoming ultra-low-NOx (oxides of nitrogen) emissions standard and in-use NOx emissions requirement requires engine manufacturers to further reduce tailpipe NOx emissions by over 90% from the current United States Environmental Protection Agency 2010 heavy-duty emissions standard. To meet ultra-low NOx standards, significant improvements to the NOx reduction capability of the Selective Catalytic Reduction (SCR) system is required. Low-temperature exhaust conditions and the associated fuel penalty in increasing the exhaust temperatures for improving catalyst activity is an engineering challenge to balance lowering NOx emissions while lowering fuel consumption. Cylinder Deactivation (CDA) in diesel engines has shown the ability to increase exhaust temperatures while maintaining a zero-fuel penalty. This study details the results of the performance of a CDA hardware installed in a modern heavy-duty diesel engine. The study was aimed at developing steady-state engine calibrations to maximize exhaust temperatures while realizing a zero-fuel penalty or improved BTE operation during low-load engine operating conditions for an on-road heavy-duty diesel engine. In addition, the study demonstrated the effect of CDA on lowering aftertreatment cooldown during motoring operation. The results of the study showed close to a 100°C increase in turbine out temperatures (TOT) at idle, 1,000 rpm and 1,200 rpm engine speeds with engine load at 10 and 20% of rated torque. The results also showed that deactivating three of the six cylinders during motoring operation of the low-load cycle delayed after treatment cool down and maintained exhaust temperatures above the SCR activity threshold for a longer duration.
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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) | 0.000 | 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".