Optimization of Hydrogen Energy Share in Dual-Fuel and RCCI Engines: An Energy and Exergy Study
Bibliographic record
Abstract
The growing reliance on fossil fuels for various transportation systems is causing numerous issues, including the depletion of fossil fuel reserves, escalating fuel prices, pollution, and global warming.These needs are addressed by modifying/ improving the internal combustion engines combustion strategy and having an alternative fuel that is environmentally friendly, economical to use commercially and by individuals, and adequately available.Hydrogen is considered one of the best choice as a clean fuel.For single cylinder engines, the energy and exergy efficiency obtained from the hydrogenpowered engine in HCCI, RCCI and dual-fuel mode are analyzed.Using hydrogen in internal combustion engines under lean conditions reduces NOx emissions, but it also leads to a decrease in power output.Super-charge or turbo-charge can be an option but this will result in more emissions.It will be beneficial to run it in Dual-fuel mode or RCCI mode..The simulation results indicate that brake power changes with the addition of hydrogen.In diesel-only mode, the brake power is 2808 W, but it gradually decreases as the hydrogen energy share increases to 36%.It then increases up to a brake power of 2806 W at 58% hydrogen energy share.The analysis concluded that the engine in dual-fuel mode should be operated at 58% hydrogen energy share, and at an injection timing of 17 BTDC as the power produced is the same as that in diesel-only mode, the emissions produced are manageable in terms of NO emissions, whereas the rest of the emissions it is lower and comfortably comply with the BSVI regulations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".