Effect of Blended Fuels on Specific Fuel Consumption at Varying Engine Loads Using CVCRM Engine Test Rig
Bibliographic record
Abstract
Vegetable oils are liquid fuels from renewable sources; they do not over-burden the environment with emissions. Vegetable oils have potential for making marginal land productive by their property of nitrogen fixation in the soil. Their production requires lesser energy input in production. They have higher energy content than other energy crops like alcohol. Vegetable oils can be successfully used in CI engine, through engine modifications and fuel modifications because vegetable oil in its raw form cannot be used in engines. Specific fuel consumption (SFC) have been computed for various blends of soyabean and mustered oils with petrol at different engine loads in computerized variable compression ratio multi-fuel (CVCRM) engine test rig. It is concluded that out of the two mustard oil blends, 20-PRM shows the lowest specific fuel consumption at the engine loads of 2.5 Kg and 5.0 Kg, where as 15-PRM shows the lowest specific fuel consumption (SCF) at the engine loads of 7.5 Kg. The experiments also show that the lowest specific fuel consumption occurs at the engine load of 7.5 Kg with using the blend of 15-PRM out of all the engine loads considered in the study.
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 | 0.000 | 0.000 |
| 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".