A Numerical Investigation on NO2 Formation in a Natural Gas-Diesel Dual Fuel Engine
Bibliographic record
Abstract
The burning of natural gas (NG) in compression ignition dual fuel engines has been highlighted for its fuel flexibility, higher thermal efficiency and reduced particulate matter (PM) emissions. Recent research has reported the significant impact of the introduction of NG to the intake port on nitrogen dioxide (NO2) emissions, particularly at the low loads. However, the research on the mechanism of NO2 formation in dual fuel engines has not been reported. This research simulates the formation and destruction of NO2 in a NG-diesel dual fuel engine using commercial CFD software CONVERGE coupled with a reduced primary reference fuel (PRF) mechanism consisting of 45 species and 142 reactions. The model was validated by comparing the simulated cylinder pressure, heat release rate, and nitrogen oxides (NOx) emissions with experimental data. The validated model was used to simulate the formation and destruction of NO2 in a NG-diesel dual fuel engine. The formation of NO2 and its correlation with the local concentration of nitric oxide (NO), methane, and temperature were examined and discussed. It was revealed that NO2 was mainly formed in the interface region between the hot NO-containing combustion products and the relatively cool unburnt methane-air mixture. NO2 formed at the early combustion stage is usually destructed to NO after the complete oxidation of methane and n-heptane, while NO2 formed during the post-combustion process would survive and exit the engine. This was supported by the distribution of NO and NO2 in the equivalence ratio (ER)-T diagram. A detailed analysis of the chemical reactions occurring in the NO2 containing zone consisting of NO2, NO, O2, methane, etc., was conducted using a quasi-homogeneous constant volume model to identify the key reactions and species dominating NO2 formation and destruction. The HO2 produced during the post combustion process of methane was identified as the primary species dominating the formation of NO2. The simulation revealed the key reaction path for the formation of HO2 noted as CH4->CH3->CH2O->HCO->HO2, with conversion ratios of 98%, 74%, 90%, 98%, accordingly. The backward reaction of OH+NO2 = NO+HO2 consumed 34% of HO2 for the production of NO2. It was concluded that the increased NO2 emissions from NG-diesel dual fuel engines was formed during the post combustion process due to higher concentration of HO2 produced during the oxidation process of the unburned methane at low temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".