Dynamic emission analysis of a hydrogen/diesel dual-fuel engine using clustering method
Bibliographic record
Abstract
Internal combustion engines play a crucial role in transportation and agriculture, yet diesel-powered heavy and medium duty freight vehicles are significant sources of CO 2 and other exhaust emissions. Retrofitting existing diesel engines to dual-fuel systems is a promising solution to reduce these emissions. Among various dual-fuel configurations, the hydrogen/diesel dual-fuel (HDDF) engine with port injection is particularly effective in lowering exhaust emissions with minimal modifications to the engine. A detailed emissions analysis of an HDDF engine controlled by a data-driven predictive controller is presented. This controller tracks a transient load profile and optimizes diesel and hydrogen injections while respecting engine durability constraints. A comparison with 100% diesel engine operation reveals that the HDDF engine reduced carbon dioxide, carbon monoxide, and non-methane hydrocarbon emissions by 57%, 75%, and 37%, and particulate matter (PM) emission of the HDDF is one seventh of the diesel. Despite these benefits, one challenge is elevated nitrogen oxides (NO x ) emissions at high loads due to the hot flame temperature. A k -means clustering method was utilized to categorize key parameters — indicated mean effective pressure (IMEP), NO x , PM , and hydrogen energy share (HES) — into three groups: low, medium, and high. Results indicated that maintaining the same NO x and PM levels for increasing load required engine operation adjustments of: increasing diesel injection timing, reducing HES to adjust NO x level, and increasing both HES and diesel start of pre-injection to adjust PM level. The data-driven predictive controller and its cost function are used to set this balance between hydrogen and diesel injections.
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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) | 0.001 | 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) | 0.001 | 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".