NOx Measurement and Characterization in a Gaseous Fueled High-Pressure Direct-Injection Engine
Bibliographic record
Abstract
Heavy-duty (HD) vehicles are a crucial part of the transportation sector; however, strict governmental regulations will require future HD vehicles to meet even more rigid NOx emission standards than what already exist. The use of natural gas (NG) as the primary fuel in HD vehicles can immediately reduce the NOx emissions through lower flame temperatures as compared to traditional diesel and can serve as a precursor to even less carbon intensive fuels as they become more readily available. Pilot ignited direct injection natural gas (PIDING) engine technology is one example of how NG can be used in HD vehicles while maintaining diesel-like efficiency. However, NOx emissions still need to be mitigated to avoid negative air quality effects. Exhaust gas recirculation (EGR) is known to reduce in-cylinder temperatures and thus reduce in-cylinder NOx emissions in diesel engines, but the effects of EGR are not as well understood in PIDING engines. The intent of this study is to develop a better understanding of the sensitivity of NOx to the specific effects of EGR in PIDING engines by experimentally identifying the limits of EGR on a single cylinder research engine (SCRE). Two different equivalence ratios (φ) of 0.6 and 0.7 were used while maintaining engine load at 12 bar GIMEP, combustion phasing, and engine speed throughout an EGR sweep. The maximum EGR rate tested was ∼50% for each φ. Combustion instability (measured by the coefficient of variability (COV) of peak cylinder pressure (PCP) and GIMEP) increased by 2 and 3% at maximum EGR for φ = 0.6 and 0.7 respectively. NOx emissions were reduced ∼80% up to 25% EGR. However, NOx sensitivity to the effects of EGR diminish significantly at rates above 35%. The inverse is also true for particulate matter (PM) and methane in that these emissions significantly increase at EGR rates above 35%. Lastly, exhaust mounted electrochemical NOx sensors were found to be effective and comparable to lab-grade emissions analyzers while being more cost effective and less intrusive.
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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 | 0.001 | 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".