Mode Switching to Improve Low Load Efficiency of an Ethanol-Diesel Dual-Fuel Engine
Bibliographic record
Abstract
The dual-fuel application using ethanol and diesel fuels can substantially improve the classical trade-off between oxides of nitrogen (NOx) and smoke, especially at moderate-to-high load conditions. However, at low engine load levels, the use of a low reactivity fuel in the dual-fuel application usually leads to increased incomplete combustion products that in turn result in a significant reduction of the engine thermal efficiency. In this work, engine tests are conducted on a high compression ratio, single cylinder dual-fuel engine that incorporates the diesel direct-injection and ethanol port-injection. Engine load levels are identified, at which, diesel combustion offers better efficiency than the dual-fuel combustion while attaining low NOx and smoke emissions. Thereafter, a cycle-to-cycle based closed-loop controller is implemented for the combustion phasing and engine load control in both the diesel and dual-fuel combustion regimes. The controller actively regulates the fuelling rates of both fuels and dynamically controls the injection timing of the diesel direct-injection. A mode switching scheme is developed and implemented in the closed-loop controller to switch between diesel and dual-fuel combustion modes for optimized emissions and efficiency, without compromising the combustion stability. The switching algorithm is validated through steady-state and transient engine tests with continuous measurements of fuel flow, engine load, NOx and PM emissions to demonstrate the effectiveness of balancing the efficiency and emissions. By applying the mode switching strategy in the dual-fuel engine, stable, efficient, and clean combustion is achieved from idle to full-load conditions.
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) | 0.000 | 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".