Experimental study on performance and emission control of a single cylinder four stroke tri charged RCCI engine with variable injection timing
Bibliographic record
Abstract
The turbo-lag is a big problem associated with a turbocharger. A crank-driven supercharger consumes the output power of the engine and tends to produce more NO X . The problem related to the turbocharger and supercharger can be solved by employing the tri-charged technology, which combines a crank-driven supercharger, turbocharger, and electric-driven supercharger. The effect of tri-charged technology in combination with reactivity controlled at different injection timing has not been investigated. The present study performed experimental investigations on a single-cylinder, 4-stroke tri-charged reactivity controlled compression ignition (RCCI) engine. Investigations were carried out for super, turbo, twin, and tri-charged modes by varying the load from 2 kg to 12 kg with 2 kg intervals for all operating conditions. The RCCI engine was suitably modified for dual-fuel by incorporating a compressed natural gas (CNG) port fuel injection system with variable injection timing (2ms, 4ms, and 6ms). Different parameters were compared, namely brake power, brake-specific fuel consumption, thermal efficiency, volumetric efficiency, and emissions, and found significant improvement with the tri-charged mode. Tri-charged experimental results show considerable brake power improvement by 0.6 kW and thermal and volumetric efficiency improved by 1.82% and 7.1%, respectively, compared with a conventional (Naturally aspirated engine) engine. The emissions trends are adequately captured with the modification to tri-charged boosting, especially for HC, CO, and NO X , which are reduced to a great extent at a relatively low air-fuel equivalence ratio. Therefore, it can be concluded that the RCCI operating range extended with the tri-charged boost pressure, especially at low load capacity.
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) | 0.001 | 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 | 0.000 |
| 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".