A Study on the High Load Operation of a Natural Gas-Diesel Dual-Fuel Engine
Bibliographic record
Abstract
Diesel fueled compression ignition engines are widely used in power generation and freight transport owing to their high fuel conversion efficiency and ability to operate reliably for long periods of time at high loads. However, such engines generate significant amounts of carbon dioxide (CO 2 ), nitrogen oxides (NOx), and particulate matter (PM) emissions. One solution to reduce the CO 2 and particulate matter emissions of diesel engines while maintaining their efficiency and reliability is natural gas (NG)-diesel dual-fuel combustion. In addition to methane emissions, the temperatures of the diesel injector tip and exhaust gas can also be concerns for dual-fuel engines at medium and high load operating conditions. In this study, a single cylinder NG-diesel dual-fuel research engine is operated at two high load conditions (75% and 100% load). NG fraction and diesel direct injection (DI) timing are two of the simplest control parameters for optimization of diesel engines converted to dual-fuel engines. In addition to studying the combined impact of these parameters on combustion and emissions performance, another unique aspect of this research is the measurement of the diesel injector tip temperature which can predict potential coking issues in dual-fuel engines. Results show that increasing NG fraction and advancing diesel direct injection timing can increase the injector tip temperature. With increasing NG fraction, while the methane emissions increase, the equivalent CO 2 emissions (cumulative greenhouse gas effect of CO 2 and CH 4 ) of the engine decrease. Increasing NG fraction also improves the brake thermal efficiency of the engine though NOx emissions increase. By optimizing the combustion phasing through control of the DI timing, brake thermal efficiencies of the order of ∼42% can be achieved. At high loads, advanced diesel DI timings typically correspond to the higher maximum cylinder pressure, maximum pressure rise rate, brake thermal efficiency and NOx emissions, and lower soot, CO, and CO 2 -equivalent emissions.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".