Operation of a Compression Ignition Engine at Idling Load Under Simulated Cold Weather Conditions
Bibliographic record
Abstract
Abstract Heavy-duty compression ignition (CI) engines can be subject to long periods of idling during their duty cycles. In colder climates, getting the engine and exhaust aftertreatment (EAT) system to ideal operating temperatures can be challenging under such idling and low load conditions. This may lead to high emissions of criteria pollutants especially nitrogen oxides (NOx). Increasing the engine load and idling speed may help mitigate NOx emissions but typically leads to higher greenhouse gas emissions. Therefore, the objective of this study is to evaluate the performance of a heavy-duty CI engine operating at various idling conditions and determine suitability for exhaust aftertreatment system operation. Tests are conducted on a heavy-duty single cylinder CI research engine fueled with diesel. As per the United States' (U.S.) Code of Federal Regulations (CFR), CI engines can be certified for the optional Clean Idle NOx emission standard. A corresponding two-mode Clean Idle Test (CIT) is specified in CFR which requires the engine to be operating in fully warmed-up condition at two engine speeds. Steady-state tests are conducted at these two engine speeds of 650 and 1100 rpm, and cold weather operation is simulated by operating the engine at suboptimal coolant, lubricant, and intake air temperatures. The steady-state tests are used to determine suitable diesel injection timing for performing the CIT under standard and simulated cold weather conditions. At the lowest load and 650 rpm idling speed, use of multiple injections and exhaust gas recirculation (EGR) are insufficient for raising the exhaust gas temperature above 200 °C. A combination of higher load, EGR, and 1100 rpm idling speed can create conditions for light-off selective catalytic reduction (LO-SCR) operation at the expense of higher fuel consumption. The modified CIT results show that the optional Clean Idle NOx emission standards can be met with the use of EGR at both test modes. The mode 2 conditions at 1100 rpm may allow LO-SCR activation as well. There are also distinct differences between results under fully and partially warmed-up conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".