A study on effect of engine operating parameters on NOx emissions and exhaust temperatures of a heavy-duty diesel engine during idling
Bibliographic record
Abstract
Though NOx emissions from on-road heavy duty vehicles (HDVs) have reduced drastically over the past three decades, upcoming North American regulations may call for further reductions. Lowering NOx emissions below current levels is challenging, especially at lower operating loads such as idling and/or colder climates when the exhaust gas temperature is insufficient for high NOx conversion in the HDV’s exhaust after treatment (EAT) system. This paper investigates the impact of various engine operating parameters on the three-way trade-off between NOx emissions, exhaust temperature, and fuel consumption using a single-cylinder, heavy-duty diesel engine outfitted with a conventional EAT system and diesel exhaust fluid (DEF) injection. The engine parameters investigated include exhaust gas recirculation (EGR) ratio, intake pressure, intake temperature, injection pressure, multiple injections, and engine fluid temperatures. In order to simulate cold climate operation, the baseline intake air, coolant, and lubricating oil temperatures are maintained at 10°C, 40°C, and 50°C, respectively. Results show that using a moderate level of EGR has the dual benefit of reducing the engine-out NOx and increasing the intake charge temperature. Increasing the intake or injection pressure does not result in significant benefit in improving the three-way trade-off. However, using double injection with the second injection at the start of the expansion stroke can help reduce NOx emissions and increase exhaust temperature with marginal impact on fuel efficiency. Based on the parametric study, three particular engine operating conditions are chosen for further investigation with the EAT online. At an identical DEF dosing rate, the rates of NOx reduction in the SCR are similar for the three conditions (∼96%), and the lowest tail-pipe NOx observed for this study is 0.032 g/kWh.
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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) | 0.001 | 0.001 |
| 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".