Investigations on generation methods for oxy-hydrogen gas, its blending with conventional fuels and effect on the performance of internal combustion engine
Bibliographic record
Abstract
In order to overcome the drawbacks of the regular petroleum fuel, it is the need of time to completely or partially replace the petroleum fuel. But alternative options to petroleum fuel are having disadvantages. An electric or compressed air driven cars cannot be used where high torque is required or using hydrogen as fuel requires very costly storage equipments. In this research work an attempt has been made to reduce the drawbacks of petroleum fuels. Electrolysis of water can give us hydrogen in form of oxy-hydrogen gas which can be used as an alternative fuel for any internal combustion engine. This research paper discusses various methods designed for the production of oxy-hydrogen gas. Later blend of ‘oxy-hydrogen gas’ and petrol or diesel is used instead of only petrol/diesel to study the influence of the ‘oxy-hydrogen gas’ on the performance of the internal combustion engine. Oxy-hydrogen gas is an enriched mixture of ‘hydrogen’ and ‘oxygen’ bonded together molecularly and magnetically. Oxy-hydrogen gas is produced by electrolysis of water using caustic soda or KOH as the catalyst. Presence of ‘oxy-hydrogen gas’ during combustion process decreases the ‘brake specific fuel consumption’ and also increases the ‘brake thermal efficiency’. Water is one of the by-products of the combustion process which also decreases the temperature of the combustion process. It is safe to use ‘oxy-hydrogen gas’ as it is not stored but is produced and used as and when required. Together with ‘brake thermal efficiency’ engine shows improvement in the ‘brake thermal efficiency’ with the blend of fuel. All together it has been observed that the blend of ‘oxy-hydrogen gas’ and petrol instead of only conventional fuel improves the performance of the engine. Key words: Oxy-hydrogen gas, alternative fuels.
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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) | 0.000 | 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 | 0.001 |
| 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".